PubMed Mentions
The links below are to publications on PubMed referring to The Cancer Imaging Archive (TCIA). This list is gathered weekly from PubMed automatically and was last updated on December 17, 2025.
| Publication/References | |
| Are shape morphologies associated with survival? A potential shape-based biomarker predicting survival in lung cancer. Description: Saad, Maliazurina, et al. Are shape morphologies associated with survival? A potential shape-based biomarker predicting survival in lung cancer. ''J Cancer Res Clin Oncol''. 2019 Dec; '''145''' (12):2937-2950 | |
| Fusion Radiomics Features from Conventional MRI Predict MGMT Promoter Methylation Status in Lower Grade Gliomas. Description: Jiang, Chendan, et al. Fusion Radiomics Features from Conventional MRI Predict MGMT Promoter Methylation Status in Lower Grade Gliomas. ''Eur J Radiol''. 2019 Dec; '''121''': 108714 | |
| Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. Description: Ibrahim, A, et al. Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. ''Methods''. 2021 Apr; '''188''': 20-29 | |
| The next horizon in precision oncology: Proteogenomics to inform cancer diagnosis and treatment. Description: Rodriguez, Henry, et al. The next horizon in precision oncology: Proteogenomics to inform cancer diagnosis and treatment. ''Cell''. 2021 Apr 1; '''184''' (7):1661-1670 | |
| Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer. Description: Zeng, Hao, et al. Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer. ''Gynecol Oncol''. 2021 Oct; '''163''' (1):171-180 | |
| A multi-object deep neural network architecture to detect prostate anatomy in T2-weighted MRI: Performance evaluation. Description: Baldeon-Calisto, Maria, et al. A multi-object deep neural network architecture to detect prostate anatomy in T2-weighted MRI: Performance evaluation. ''Front Nucl Med''. 2022; '''2''': 1083245 | |
| Development and verification of radiomics framework for computed tomography image segmentation. Description: Gu, Jiabing, et al. Development and verification of radiomics framework for computed tomography image segmentation. ''Med Phys''. 2022 Oct; '''49''' (10):6527-6537 | |
| Patient-Specific Magnetic Catheters for Atraumatic Autonomous Endoscopy. Description: Pittiglio, Giovanni, et al. Patient-Specific Magnetic Catheters for Atraumatic Autonomous Endoscopy. ''Soft Robot''. 2022 Dec; '''9''' (6):1120-1133 | |
| The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy. Description: Crombe, Amandine, et al. The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy. ''Cancer Commun (Lond)''. 2022 Dec; '''42''' (12):1288-1313 | |
| An intravenous pancreatic cancer therapeutic: Characterization of CRISPR/Cas9n-modified Clostridium novyi-Non Toxic. Description: Dailey, Kaitlin M, et al. An intravenous pancreatic cancer therapeutic: Characterization of CRISPR/Cas9n-modified Clostridium novyi-Non Toxic. ''PLoS One''. 2023; '''18''' (11):e0289183 | |
| Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme. Description: Zhang, Di, et al. Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme. ''Front Med (Lausanne)''. 2023; '''10''': 1271687 | |
| CT radiomics for noninvasively predicting NQO1 expression levels in hepatocellular carcinoma. Description: He, Zenglei, et al. CT radiomics for noninvasively predicting NQO1 expression levels in hepatocellular carcinoma. ''PLoS One''. 2023; '''18''' (9):e0290900 | |
| Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients. Description: Dammu, Hongyi, et al. Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients. ''PLoS One''. 2023; '''18''' (1):e0280148 | |
| Med-ImageTools: An open-source Python package for robust data processing pipelines and curating medical imaging data. Description: Kim, Sejin, et al. Med-ImageTools: An open-source Python package for robust data processing pipelines and curating medical imaging data. ''F1000Res''. 2023; '''12''': 118 | |
| SIFT-GVF-based lung edge correction method for correcting the lung region in CT images. Description: Li, Xin, et al. SIFT-GVF-based lung edge correction method for correcting the lung region in CT images. ''PLoS One''. 2023; '''18''' (2):e0282107 | |
| The involvement of brain regions associated with lower KPS and shorter survival time predicts a poor prognosis in glioma. Description: Bao, Hongbo, et al. The involvement of brain regions associated with lower KPS and shorter survival time predicts a poor prognosis in glioma. ''Front Neurol''. 2023; '''14''': 1264322 | |
| Translating Data Science Results into Precision Oncology Decisions: A Mini Review. Description: Capobianco, Enrico, et al. Translating Data Science Results into Precision Oncology Decisions: A Mini Review. ''J Clin Med''. 2023 Jan 5; '''12''' (2): | |
| Molecular hallmarks of breast multiparametric magnetic resonance imaging during neoadjuvant chemotherapy. Description: Lin, Peng, et al. Molecular hallmarks of breast multiparametric magnetic resonance imaging during neoadjuvant chemotherapy. ''Radiol Med''. 2023 Jan 21; 1-13 | |
| Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer. Description: Liu, Qian, et al. Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer. ''Biomark Res''. 2023 Jan 24; '''11''' (1):9 | |
| Prognostic value of (18)F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer. Description: Wang, Bingzhen, et al. Prognostic value of (18)F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer. ''EJNMMI Res''. 2023 Feb 13; '''13''' (1):14 | |
| Computed tomography-based radiomics prediction of CTLA4 expression and prognosis in clear cell renal cell carcinoma. Description: He, Hongchao, et al. Computed tomography-based radiomics prediction of CTLA4 expression and prognosis in clear cell renal cell carcinoma. ''Cancer Med''. 2023 Mar; '''12''' (6):7627-7638 | |
| New insights into glioma frequency maps: From genetic and transcriptomic correlate to survival prediction. Description: Bao, Hongbo, et al. New insights into glioma frequency maps: From genetic and transcriptomic correlate to survival prediction. ''Int J Cancer''. 2023 Mar 1; '''152''' (5):998-1012 | |
| Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline. Description: Ye, Zezhong, et al. Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline. ''medRxiv''. 2023 Mar 6; | |
| An Online Mammography Database with Biopsy Confirmed Types. Description: Cai, Hongmin, et al. An Online Mammography Database with Biopsy Confirmed Types. ''Sci Data''. 2023 Mar 7; '''10''' (1):123 | |
| Direct prediction of Homologous Recombination Deficiency from routine histology in ten different tumor types with attention-based Multiple Instance Learning: a development and validation study. Description: Lavinia Loeffler, Chiara Maria, et al. Direct prediction of Homologous Recombination Deficiency from routine histology in ten different tumor types with attention-based Multiple Instance Learning: a development and validation study. ''medRxiv''. 2023 Mar 10; | |
| An immune indicator based on BTK and DPEP2 identifies hot and cold tumors and clinical treatment outcomes in lung adenocarcinoma. Description: Han, Tao, et al. An immune indicator based on BTK and DPEP2 identifies hot and cold tumors and clinical treatment outcomes in lung adenocarcinoma. ''Sci Rep''. 2023 Mar 29; '''13''' (1):5153 | |
| MTF1 has the potential as a diagnostic and prognostic marker for gastric cancer and is associated with good prognosis. Description: He, Jin, et al. MTF1 has the potential as a diagnostic and prognostic marker for gastric cancer and is associated with good prognosis. ''Clin Transl Oncol''. 2023 Apr 24; 1-11 | |
| Clinical applications of artificial intelligence in radiology. Description: Mello-Thoms, Claudia, et al. Clinical applications of artificial intelligence in radiology. ''Br J Radiol''. 2023 Apr 26; '''96''' (1150):20221031 | |
| Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features. Description: Wang, Fen, et al. Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features. ''Sci Rep''. 2023 Jun 8; '''13''' (1):9302 | |
| Assessment of brain cancer atlas maps with multimodal imaging features. Description: Capobianco, Enrico, et al. Assessment of brain cancer atlas maps with multimodal imaging features. ''J Transl Med''. 2023 Jun 12; '''21''' (1):385 | |
| Expert-level pediatric brain tumor segmentation in a limited data scenario with stepwise transfer learning. Description: Boyd, Aidan, et al. Expert-level pediatric brain tumor segmentation in a limited data scenario with stepwise transfer learning. ''medRxiv''. 2023 Jun 30; | |
| HLA-DQA1 expression is associated with prognosis and predictable with radiomics in breast cancer. Description: Zhou, JingYu, et al. HLA-DQA1 expression is associated with prognosis and predictable with radiomics in breast cancer. ''Radiat Oncol''. 2023 Jul 11; '''18''' (1):117 | |
| Tumor and local lymphoid tissue interaction determines prognosis in high-grade serous ovarian cancer. Description: Lu, Haonan, et al. Tumor and local lymphoid tissue interaction determines prognosis in high-grade serous ovarian cancer. ''Cell Rep Med''. 2023 Jul 18; '''4''' (7):101092 | |
| Imaging-Based Versus Pathologic Survival Stratifications of Diffuse Glioma According to the 2021 WHO Classification System. Description: Lee, So Jeong, et al. Imaging-Based Versus Pathologic Survival Stratifications of Diffuse Glioma According to the 2021 WHO Classification System. ''Korean J Radiol''. 2023 Aug; '''24''' (8):772-783 | |
| SAMPLER: Empirical distribution representations for rapid analysis of whole slide tissue images. Description: Mukashyaka, Patience, et al. SAMPLER: Empirical distribution representations for rapid analysis of whole slide tissue images. ''bioRxiv''. 2023 Aug 3; | |
| SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers. Description: Al-Tashi, Qasem, et al. SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers. ''Patterns (N Y)''. 2023 Aug 11; '''4''' (8):100777 | |
| CT radiomics prediction of CXCL9 expression and survival in ovarian cancer. Description: Gu, Rui, et al. CT radiomics prediction of CXCL9 expression and survival in ovarian cancer. ''J Ovarian Res''. 2023 Aug 30; '''16''' (1):180 | |
| A Large Open Access Dataset of Brain Metastasis 3D Segmentations with Clinical and Imaging Feature Information. Description: Ramakrishnan, Divya, et al. A Large Open Access Dataset of Brain Metastasis 3D Segmentations with Clinical and Imaging Feature Information. ''ArXiv''. 2023 Sep 12; | |
| Prediction of cancer treatment response from histopathology images through imputed transcriptomics. Description: Hoang, Danh-Tai, et al. Prediction of cancer treatment response from histopathology images through imputed transcriptomics. ''Res Sq''. 2023 Sep 15; | |
| A multimodal radiomic machine learning approach to predict the LCK expression and clinical prognosis in high-grade serous ovarian cancer. Description: Zhan, Feng, et al. A multimodal radiomic machine learning approach to predict the LCK expression and clinical prognosis in high-grade serous ovarian cancer. ''Sci Rep''. 2023 Sep 29; '''13''' (1):16397 | |
| Radiological Study of Atlas Arch Defects with Meta-Analysis and a Proposed New Classification. Description: Suphamungmee, Worawit, et al. Radiological Study of Atlas Arch Defects with Meta-Analysis and a Proposed New Classification. ''Asian Spine J''. 2023 Oct; '''17''' (5):975-984 | |
| ARPC5 acts as a potential prognostic biomarker that is associated with cell proliferation, migration and immune infiltrate in gliomas. Description: Ming, Yue, et al. ARPC5 acts as a potential prognostic biomarker that is associated with cell proliferation, migration and immune infiltrate in gliomas. ''BMC Cancer''. 2023 Oct 3; '''23''' (1):937 | |
| Noninvasive radiomic analysis of enhanced CT predicts CTLA4 expression and prognosis in head and neck squamous cell carcinoma. Description: Zhu, Yeping, et al. Noninvasive radiomic analysis of enhanced CT predicts CTLA4 expression and prognosis in head and neck squamous cell carcinoma. ''Sci Rep''. 2023 Oct 5; '''13''' (1):16782 | |
| Association of graph-based spatial features with overall survival status of glioblastoma patients. Description: Lee, Joonsang, et al. Association of graph-based spatial features with overall survival status of glioblastoma patients. ''Sci Rep''. 2023 Oct 9; '''13''' (1):17046 | |
| CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations. Description: Xia, Tian, et al. CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations. ''J Digit Imaging''. 2023 Dec; '''36''' (6):2356-2366 | |
| Profiling regulatory T lymphocytes within the tumor microenvironment of breast cancer via radiomics. Description: Jiang, Wenying, et al. Profiling regulatory T lymphocytes within the tumor microenvironment of breast cancer via radiomics. ''Cancer Med''. 2023 Dec; '''12''' (24):21861-21872 | |
| Deep learning-based segmentation of multisite disease in ovarian cancer. Description: Buddenkotte, Thomas, et al. Deep learning-based segmentation of multisite disease in ovarian cancer. ''Eur Radiol Exp''. 2023 Dec 7; '''7''' (1):77 | |
| Lesion detection in women breast's dynamic contrast-enhanced magnetic resonance imaging using deep learning. Description: Saikia, Sudarshan, et al. Lesion detection in women breast's dynamic contrast-enhanced magnetic resonance imaging using deep learning. ''Sci Rep''. 2023 Dec 18; '''13''' (1):22555 | |
| A glycolysis-related signature to improve the current treatment and prognostic evaluation for breast cancer. Description: Feng, Sijie, et al. A glycolysis-related signature to improve the current treatment and prognostic evaluation for breast cancer. ''PeerJ''. 2024; '''12''': e17861 | |
| A practical guide to FAIR data management in the age of multi-OMICS and AI. Description: Mugahid, Douaa, et al. A practical guide to FAIR data management in the age of multi-OMICS and AI. ''Front Immunol''. 2024; '''15''': 1439434 | |
| Comparing the performance of a deep learning-based lung gross tumour volume segmentation algorithm before and after transfer learning in a new hospital. Description: Kulkarni, Chaitanya, et al. Comparing the performance of a deep learning-based lung gross tumour volume segmentation algorithm before and after transfer learning in a new hospital. ''BJR Open''. 2024 Jan; '''6''' (1):tzad008 | |
| Comparison of radiomics-based machine-learning classifiers for the pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer. Description: Li, Xue, et al. Comparison of radiomics-based machine-learning classifiers for the pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer. ''PeerJ''. 2024; '''12''': e17683 | |
| Enhancing prognostic prediction in hepatocellular carcinoma post-TACE: a machine learning approach integrating radiomics and clinical features. Description: Zhang, Mingqi, et al. Enhancing prognostic prediction in hepatocellular carcinoma post-TACE: a machine learning approach integrating radiomics and clinical features. ''Front Med (Lausanne)''. 2024; '''11''': 1419058 | |
| Intensity scaling of conventional brain magnetic resonance images avoiding cerebral reference regions: A systematic review. Description: Wiltgen, Tun, et al. Intensity scaling of conventional brain magnetic resonance images avoiding cerebral reference regions: A systematic review. ''PLoS One''. 2024; '''19''' (3):e0298642 | |
| The peritumoral edema index and related mechanisms influence the prognosis of GBM patients. Description: Fang, Zhansheng, et al. The peritumoral edema index and related mechanisms influence the prognosis of GBM patients. ''Front Oncol''. 2024; '''14''': 1417208 | |
| Use of fractals in determining the malignancy degree of lung nodules. Description: Amador-Legon, Noel Victor, et al. Use of fractals in determining the malignancy degree of lung nodules. ''Front Med Technol''. 2024; '''6''': 1362688 | |
| Focus stacking single-event particle radiography for high spatial resolution images and 3D feature localization. Description: Volz, Lennart, et al. Focus stacking single-event particle radiography for high spatial resolution images and 3D feature localization. ''Phys Med Biol''. 2024 Jan 10; '''69''' (2): | |
| A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images. Description: Sampath, Kanimozhi, et al. A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images. ''Sci Rep''. 2024 Jan 25; '''14''' (1):2144 | |
| Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme. Description: Luan, Jixin, et al. Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme. ''J Transl Med''. 2024 Jan 26; '''22''' (1):107 | |
| MRI-derived radiomics assessing tumor-infiltrating macrophages enable prediction of immune-phenotype, immunotherapy response and survival in glioma. Description: Chen, Di, et al. MRI-derived radiomics assessing tumor-infiltrating macrophages enable prediction of immune-phenotype, immunotherapy response and survival in glioma. ''Biomark Res''. 2024 Jan 31; '''12''' (1):14 | |
| A clinically relevant computed tomography (CT) radiomics strategy for intracranial rodent brain tumour monitoring. Description: Connor, Kate, et al. A clinically relevant computed tomography (CT) radiomics strategy for intracranial rodent brain tumour monitoring. ''Sci Rep''. 2024 Feb 1; '''14''' (1):2720 | |
| Development of End-to-End AI-Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation. Description: Santinha, Joao, et al. Development of End-to-End AI-Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation. ''J Imaging Inform Med''. 2024 Feb; '''37''' (1):31-44 | |
| A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI. Description: Wei, Ruili, et al. A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI. ''J Cancer Res Clin Oncol''. 2024 Feb 2; '''150''' (2):73 | |
| Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis. Description: Boubnovski Martell, Marc, et al. Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis. ''NPJ Precis Oncol''. 2024 Feb 3; '''8''' (1):28 | |
| A 4D-CBCT correction network based on contrastive learning for dose calculation in lung cancer. Description: Cao, Nannan, et al. A 4D-CBCT correction network based on contrastive learning for dose calculation in lung cancer. ''Radiat Oncol''. 2024 Feb 9; '''19''' (1):20 | |
| AI applications in musculoskeletal imaging: a narrative review. Description: Gitto, Salvatore, et al. AI applications in musculoskeletal imaging: a narrative review. ''Eur Radiol Exp''. 2024 Feb 15; '''8''' (1):22 | |
| CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer. Description: Wang, Jiexiao, et al. CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer. ''BMC Med Imaging''. 2024 Feb 15; '''24''' (1):45 | |
| Reducing image artifacts in sparse projection CT using conditional generative adversarial networks. Description: Usui, Keisuke, et al. Reducing image artifacts in sparse projection CT using conditional generative adversarial networks. ''Sci Rep''. 2024 Feb 16; '''14''' (1):3917 | |
| CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies. Description: Gitto, Salvatore, et al. CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies. ''Insights Imaging''. 2024 Feb 27; '''15''' (1):54 | |
| A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information. Description: Ramakrishnan, Divya, et al. A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information. ''Sci Data''. 2024 Feb 29; '''11''' (1):254 | |
| Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer. Description: Huang, Zi Huai, et al. Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer. ''J Transl Med''. 2024 Mar 2; '''22''' (1):226 | |
| Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept. Description: Salehjahromi, Morteza, et al. Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept. ''Cell Rep Med''. 2024 Mar 19; '''5''' (3):101463 | |
| Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response. Description: Chen, Ziqiang, et al. Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response. ''NPJ Precis Oncol''. 2024 Mar 22; '''8''' (1):73 | |
| A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study. Description: Lai, Jianguo, et al. A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study. ''Int J Surg''. 2024 Apr 1; '''110''' (4):2162-2177 | |
| Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study. Description: Chen, Siteng, et al. Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study. ''Int J Surg''. 2024 May 1; '''110''' (5):2970-2977 | |
| Risk assessment model based on nucleotide metabolism-related genes highlights SLC27A2 as a potential therapeutic target in breast cancer. Description: Zhang, Bo, et al. Risk assessment model based on nucleotide metabolism-related genes highlights SLC27A2 as a potential therapeutic target in breast cancer. ''J Cancer Res Clin Oncol''. 2024 May 16; '''150''' (5):258 | |
| Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images. Description: Shahram, Mohammad Amin, et al. Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images. ''BMC Neurosci''. 2024 May 25; '''25''' (1):26 | |
| Predicting CD27 expression and clinical prognosis in serous ovarian cancer using CT-based radiomics. Description: Zhang, Chen, et al. Predicting CD27 expression and clinical prognosis in serous ovarian cancer using CT-based radiomics. ''J Ovarian Res''. 2024 Jun 22; '''17''' (1):131 | |
| Solving the Pervasive Problem of Protocol Non-Compliance in MRI using an Open-Source tool mrQA. Description: Sinha, Harsh, et al. Solving the Pervasive Problem of Protocol Non-Compliance in MRI using an Open-Source tool mrQA. ''Neuroinformatics''. 2024 Jul; '''22''' (3):297-315 | |
| Preoperative prediction of MGMT promoter methylation in glioblastoma based on multiregional and multi-sequence MRI radiomics analysis. Description: Li, Lanqing, et al. Preoperative prediction of MGMT promoter methylation in glioblastoma based on multiregional and multi-sequence MRI radiomics analysis. ''Sci Rep''. 2024 Jul 11; '''14''' (1):16031 | |
| Mask region-based convolutional neural network and VGG-16 inspired brain tumor segmentation. Description: Basha, Niha Kamal, et al. Mask region-based convolutional neural network and VGG-16 inspired brain tumor segmentation. ''Sci Rep''. 2024 Jul 30; '''14''' (1):17615 | |
| Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer. Description: Ju, Hye-Yeon, et al. Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer. ''Biomark Res''. 2024 Aug 12; '''12''' (1):80 | |
| Tumor contour irregularity on preoperative CT predicts prognosis in renal cell carcinoma: a multi-institutional study. Description: Zhu, Pingyi, et al. Tumor contour irregularity on preoperative CT predicts prognosis in renal cell carcinoma: a multi-institutional study. ''EClinicalMedicine''. 2024 Sep; '''75''': 102775 | |
| Radiomics of multi-parametric MRI for the prediction of lung metastasis in soft-tissue sarcoma: a feasibility study. Description: Hu, Yue, et al. Radiomics of multi-parametric MRI for the prediction of lung metastasis in soft-tissue sarcoma: a feasibility study. ''Cancer Imaging''. 2024 Sep 5; '''24''' (1):119 | |
| Multicenter radio-multiomic analysis for predicting breast cancer outcome and unravelling imaging-biological connection. Description: You, Chao, et al. Multicenter radio-multiomic analysis for predicting breast cancer outcome and unravelling imaging-biological connection. ''NPJ Precis Oncol''. 2024 Sep 7; '''8''' (1):193 | |
| Three-dimensional numerical schemes for the segmentation of the psoas muscle in X-ray computed tomography images. Description: Paolucci, Giulio, et al. Three-dimensional numerical schemes for the segmentation of the psoas muscle in X-ray computed tomography images. ''BMC Med Imaging''. 2024 Sep 19; '''24''' (1):251 | |
| Contemporary Update on Clinical and Experimental Prostate Cancer Biomarkers: A Multi-Omics-Focused Approach to Detection and Risk Stratification. Description: Hachem, Sana, et al. Contemporary Update on Clinical and Experimental Prostate Cancer Biomarkers: A Multi-Omics-Focused Approach to Detection and Risk Stratification. ''Biology (Basel)''. 2024 Sep 25; '''13''' (10): | |
| Radiomics Signatures Based on Computed Tomography for Noninvasive Prediction of CXCL10 Expression and Prognosis in Ovarian Cancer. Description: Wang, Xiaohua, et al. Radiomics Signatures Based on Computed Tomography for Noninvasive Prediction of CXCL10 Expression and Prognosis in Ovarian Cancer. ''Cancer Rep (Hoboken)''. 2024 Oct; '''7''' (10):e70030 | |
| Transforming Cancer Research through Informatics. Description: Klemm, Juli D, et al. Transforming Cancer Research through Informatics. ''Cancer Discov''. 2024 Oct 4; '''14''' (10):1779-1782 | |
| Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types. Description: Loeffler, Chiara Maria Lavinia, et al. Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types. ''BMC Biol''. 2024 Oct 8; '''22''' (1):225 | |
| Constructing and exploring neuroimaging projects: a survey from clinical practice to scientific research. Description: Chen, Ziyan, et al. Constructing and exploring neuroimaging projects: a survey from clinical practice to scientific research. ''Insights Imaging''. 2024 Nov 15; '''15''' (1):272 | |
| Ranking attention multiple instance learning for lymph node metastasis prediction on multicenter cervical cancer MRI. Description: Jin, Shan, et al. Ranking attention multiple instance learning for lymph node metastasis prediction on multicenter cervical cancer MRI. ''J Appl Clin Med Phys''. 2024 Dec; '''25''' (12):e14547 | |
| GrandQC: A comprehensive solution to quality control problem in digital pathology. Description: Weng, Zhilong, et al. GrandQC: A comprehensive solution to quality control problem in digital pathology. ''Nat Commun''. 2024 Dec 16; '''15''' (1):10685 | |
| Prediction of prognosis, efficacy of lung adenocarcinoma by machine learning model based on immune and metabolic related genes. Description: Xue, Cong, et al. Prediction of prognosis, efficacy of lung adenocarcinoma by machine learning model based on immune and metabolic related genes. ''Discov Oncol''. 2024 Dec 18; '''15''' (1):778 | |
| Survival analysis of clear cell renal cell carcinoma based on radiomics and deep learning features from CT images. Description: Lu, Zhennan, et al. Survival analysis of clear cell renal cell carcinoma based on radiomics and deep learning features from CT images. ''Medicine (Baltimore)''. 2024 Dec 20; '''103''' (51):e40723 | |
| Prognostic value and immune infiltration of a tumor microenvironment-related PTPN6 in metastatic melanoma. Description: Sun, Rongyao, et al. Prognostic value and immune infiltration of a tumor microenvironment-related PTPN6 in metastatic melanoma. ''Cancer Cell Int''. 2024 Dec 28; '''24''' (1):435 | |
| Development and validation of a radiomic prediction model for TACC3 expression and prognosis in non-small cell lung cancer using contrast-enhanced CT imaging. Description: Bai, Weichao, et al. Development and validation of a radiomic prediction model for TACC3 expression and prognosis in non-small cell lung cancer using contrast-enhanced CT imaging. ''Transl Oncol''. 2025 Jan; '''51''': 102211 | |
| Exploring adult glioma through MRI: A review of publicly available datasets to guide efficient image analysis. Description: Abbad Andaloussi, Meryem, et al. Exploring adult glioma through MRI: A review of publicly available datasets to guide efficient image analysis. ''Neurooncol Adv''. 2025 Jan-Dec; '''7''' (1):vdae197 | |
| Generation of severely scoliotic subject-specific musculoskeletal models. Description: Gould, Samuele Luca, et al. Generation of severely scoliotic subject-specific musculoskeletal models. ''PLoS One''. 2025; '''20''' (12):e0336211 | |
| How the First Medical Imaging Cancer Atlas EUCAIM Was Populated: The Experience of a Reference Hospital. Description: Penades Blasco, Ana, et al. How the First Medical Imaging Cancer Atlas EUCAIM Was Populated: The Experience of a Reference Hospital. ''Open Res Eur''. 2025; '''5''': 310 | |
| Integration of histopathological image features and multi-dimensional omics data in predicting molecular features and survival in glioblastoma. Description: Huang, Yeqian, et al. Integration of histopathological image features and multi-dimensional omics data in predicting molecular features and survival in glioblastoma. ''Front Med (Lausanne)''. 2025; '''12''': 1510793 | |
| Integration of histopathological images and immunological analysis to predict M2 macrophage infiltration and prognosis in patients with serous ovarian cancer. Description: Zhao, Ling, et al. Integration of histopathological images and immunological analysis to predict M2 macrophage infiltration and prognosis in patients with serous ovarian cancer. ''Front Immunol''. 2025; '''16''': 1505509 | |
| Multimodal MRI radiomics based on habitat subregions of the tumor microenvironment for predicting risk stratification in glioblastoma. Description: Wang, Han. Multimodal MRI radiomics based on habitat subregions of the tumor microenvironment for predicting risk stratification in glioblastoma. ''PLoS One''. 2025; '''20''' (6):e0326361 | |
| Multi-platform integration of histopathological images and omics data predicts molecular features and prognosis of hepatocellular carcinoma. Description: Chen, Linyan, et al. Multi-platform integration of histopathological images and omics data predicts molecular features and prognosis of hepatocellular carcinoma. ''Front Oncol''. 2025; '''15''': 1591165 | |
| Multi-scale error-driven dense residual network for image super-resolution reconstruction. Description: Li, Xueri, et al. Multi-scale error-driven dense residual network for image super-resolution reconstruction. ''PLoS One''. 2025; '''20''' (9):e0330615 | |
| Predicting podoplanin expression and prognostic significance in high-grade glioma based on TCGA TCIA radiomics. Description: Long, Shengrong, et al. Predicting podoplanin expression and prognostic significance in high-grade glioma based on TCGA TCIA radiomics. ''PLoS One''. 2025; '''20''' (6):e0325964 | |
| The dual role of CXCL9/SPP1 polarized tumor-associated macrophages in modulating anti-tumor immunity in hepatocellular carcinoma. Description: Gu, Yu, et al. The dual role of CXCL9/SPP1 polarized tumor-associated macrophages in modulating anti-tumor immunity in hepatocellular carcinoma. ''Front Immunol''. 2025; '''16''': 1528103 | |
| Increased SOAT2 expression in aged regulatory T cells is associated with altered cholesterol metabolism and reduced anti-tumor immunity. Description: Zhang, Mingjiong, et al. Increased SOAT2 expression in aged regulatory T cells is associated with altered cholesterol metabolism and reduced anti-tumor immunity. ''Nat Commun''. 2025 Jan 13; '''16''' (1):630 | |
| AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis. Description: Lee, Myungeun, et al. AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis. ''J Imaging Inform Med''. 2025 Feb; '''38''' (1):74-83 | |
| MAZ-mediated tumor progression and immune evasion in hormone receptor-positive breast cancer: Targeting tumor microenvironment and PCLAF+ subtype-specific therapy. Description: Ni, Gaofeng, et al. MAZ-mediated tumor progression and immune evasion in hormone receptor-positive breast cancer: Targeting tumor microenvironment and PCLAF+ subtype-specific therapy. ''Transl Oncol''. 2025 Feb; '''52''': 102280 | |
| Simplifying Radiomics Workflow for Predicting Grade of Glioma: An Approach for Rapid and Reproducible Radiomics. Description: Soleymani, Yunus, et al. Simplifying Radiomics Workflow for Predicting Grade of Glioma: An Approach for Rapid and Reproducible Radiomics. ''J Biomed Phys Eng''. 2025 Feb; '''15''' (1):27-36 | |
| Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI. Description: Wald, Tassilo, et al. Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI. ''Eur Radiol Exp''. 2025 Feb 6; '''9''' (1):15 | |
| Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses. Description: Xiong, Ying, et al. Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses. ''Nat Commun''. 2025 Feb 7; '''16''' (1):1425 | |
| Molecular subtyping combined with multiomics analysis to study correlation between TACE refractoriness and tumor stemness in hepatocellular carcinoma. Description: He, Qifan, et al. Molecular subtyping combined with multiomics analysis to study correlation between TACE refractoriness and tumor stemness in hepatocellular carcinoma. ''Discov Oncol''. 2025 Feb 17; '''16''' (1):197 | |
| Tumour surface regularity predicts survival and benefit from gross total resection in IDH-wildtype glioblastoma patients. Description: Lin, Peng, et al. Tumour surface regularity predicts survival and benefit from gross total resection in IDH-wildtype glioblastoma patients. ''Insights Imaging''. 2025 Feb 17; '''16''' (1):42 | |
| MRI radiomics based on machine learning in high-grade gliomas as a promising tool for prediction of CD44 expression and overall survival. Description: Yu, Mingjun, et al. MRI radiomics based on machine learning in high-grade gliomas as a promising tool for prediction of CD44 expression and overall survival. ''Sci Rep''. 2025 Mar 3; '''15''' (1):7433 | |
| Interpretable multimodal transformer for prediction of molecular subtypes and grades in adult-type diffuse gliomas. Description: Byeon, Yunsu, et al. Interpretable multimodal transformer for prediction of molecular subtypes and grades in adult-type diffuse gliomas. ''NPJ Digit Med''. 2025 Mar 5; '''8''' (1):140 | |
| Prediction and analysis of tumor infiltrating lymphocytes across 28 cancers by TILScout using deep learning. Description: Zhang, Huibo, et al. Prediction and analysis of tumor infiltrating lymphocytes across 28 cancers by TILScout using deep learning. ''NPJ Precis Oncol''. 2025 Mar 19; '''9''' (1):76 | |
| Synthetic bone marrow images augment real samples in developing acute myeloid leukemia microscopy classification models. Description: Eckardt, Jan-Niklas, et al. Synthetic bone marrow images augment real samples in developing acute myeloid leukemia microscopy classification models. ''NPJ Digit Med''. 2025 Mar 22; '''8''' (1):173 | |
| MRI transformer deep learning and radiomics for predicting IDH wild type TERT promoter mutant gliomas. Description: Niu, Wenju, et al. MRI transformer deep learning and radiomics for predicting IDH wild type TERT promoter mutant gliomas. ''NPJ Precis Oncol''. 2025 Mar 27; '''9''' (1):89 | |
| Construction of enhanced MRI-based radiomics models using machine learning algorithms for non-invasive prediction of IL7R expression in high-grade gliomas and its prognostic value in clinical practice. Description: Zhou, Jie. Construction of enhanced MRI-based radiomics models using machine learning algorithms for non-invasive prediction of IL7R expression in high-grade gliomas and its prognostic value in clinical practice. ''J Transl Med''. 2025 Mar 31; '''23''' (1):383 | |
| Artificial intelligence predicts multiclass molecular signatures and subtypes directly from breast cancer histology: a multicenter retrospective study. Description: Zhang, Xiangyang, et al. Artificial intelligence predicts multiclass molecular signatures and subtypes directly from breast cancer histology: a multicenter retrospective study. ''Int J Surg''. 2025 Apr 1; '''111''' (4):3109-3114 | |
| CCL26 as a prognostic biomarker in hepatocellular carcinoma: integrating bioinformatics analysis, clinical validation, and radiomics score. Description: Yan, Junjun, et al. CCL26 as a prognostic biomarker in hepatocellular carcinoma: integrating bioinformatics analysis, clinical validation, and radiomics score. ''Discov Oncol''. 2025 Apr 9; '''16''' (1):502 | |
| Development of a radiomic model to predict CEACAM1 expression and prognosis in ovarian cancer. Description: Zhang, Xiaoxue, et al. Development of a radiomic model to predict CEACAM1 expression and prognosis in ovarian cancer. ''Sci Rep''. 2025 Apr 30; '''15''' (1):15259 | |
| Machine learning and single-cell analysis uncover distinctive characteristics of CD300LG within the TNBC immune microenvironment: experimental validation. Description: Zhu, Baoxi, et al. Machine learning and single-cell analysis uncover distinctive characteristics of CD300LG within the TNBC immune microenvironment: experimental validation. ''Clin Exp Med''. 2025 May 17; '''25''' (1):167 | |
| Multimodal fusion model for prognostic prediction and radiotherapy response assessment in head and neck squamous cell carcinoma. Description: Tian, Ruxian, et al. Multimodal fusion model for prognostic prediction and radiotherapy response assessment in head and neck squamous cell carcinoma. ''NPJ Digit Med''. 2025 May 23; '''8''' (1):302 | |
| Enhanced magnetic resonance imaging-based radiomics predicts CD40LG expression and survival in high-grade gliomas: a retrospective study. Description: He, Jie, et al. Enhanced magnetic resonance imaging-based radiomics predicts CD40LG expression and survival in high-grade gliomas: a retrospective study. ''Discov Oncol''. 2025 May 30; '''16''' (1):962 | |
| MRI features and prognostic evaluation in patients with subventricular zone-contacting IDH-wild-type glioblastoma. Description: Pan, Shijiao, et al. MRI features and prognostic evaluation in patients with subventricular zone-contacting IDH-wild-type glioblastoma. ''Radiol Oncol''. 2025 Jun 1; '''59''' (2):329-336 | |
| Uncertainty Estimation for Dual View X-ray Mammographic Image Registration Using Deep Ensembles. Description: Walton, William C, et al. Uncertainty Estimation for Dual View X-ray Mammographic Image Registration Using Deep Ensembles. ''J Imaging Inform Med''. 2025 Jun; '''38''' (3):1829-1845 | |
| Integration of MRI radiomics and germline genetics to predict the IDH mutation status of gliomas. Description: Nakase, Taishi, et al. Integration of MRI radiomics and germline genetics to predict the IDH mutation status of gliomas. ''NPJ Precis Oncol''. 2025 Jun 16; '''9''' (1):187 | |
| Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients. Description: Zhang, Zhe, et al. Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients. ''NPJ Precis Oncol''. 2025 Jun 19; '''9''' (1):196 | |
| Computational image and molecular analysis reveal unique prognostic features of immune architecture in African Versus European American women with endometrial cancer. Description: Azarianpour, Sepideh, et al. Computational image and molecular analysis reveal unique prognostic features of immune architecture in African Versus European American women with endometrial cancer. ''NPJ Precis Oncol''. 2025 Jun 23; '''9''' (1):203 | |
| Development and validation of a fusion model based on multi-phase contrast CT radiomics combined with clinical features for predicting Ki-67 expression in gastric cancer. Description: Song, Tianjun, et al. Development and validation of a fusion model based on multi-phase contrast CT radiomics combined with clinical features for predicting Ki-67 expression in gastric cancer. ''Biomed Rep''. 2025 Jul; '''23''' (1):118 | |
| Machine learning model for predicting tertiary lymphoid structures and treatment response in triple-negative breast cancer. Description: Lin, Yidan, et al. Machine learning model for predicting tertiary lymphoid structures and treatment response in triple-negative breast cancer. ''NPJ Precis Oncol''. 2025 Jul 1; '''9''' (1):216 | |
| Stochastic differential equation modeling approach for grading astrocytomas on brain MRI images. Description: Raisi-Nafchi, Mahsa, et al. Stochastic differential equation modeling approach for grading astrocytomas on brain MRI images. ''Sci Rep''. 2025 Jul 2; '''15''' (1):22835 | |
| Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer. Description: Nishizawa, Taishi, et al. Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer. ''J Transl Med''. 2025 Jul 10; '''23''' (1):774 | |
| Predicting exploratory thoracotomy in non-small cell lung cancer: a computed tomography based nomogram approach. Description: Dai, Fuqiang, et al. Predicting exploratory thoracotomy in non-small cell lung cancer: a computed tomography based nomogram approach. ''BMC Cancer''. 2025 Jul 10; '''25''' (1):1161 | |
| Bi-Regional Machine Learning Radiomics Based on CT Noninvasively Predicts LOX Expression Level and Overall Survival in Hepatocellular Carcinoma. Description: Gao, Kexin, et al. Bi-Regional Machine Learning Radiomics Based on CT Noninvasively Predicts LOX Expression Level and Overall Survival in Hepatocellular Carcinoma. ''Cancer Med''. 2025 Aug; '''14''' (15):e71154 | |
| Multimodal data curation via interoperability: use cases with the Medical Imaging and Data Resource Center. Description: Chen, Weijie, et al. Multimodal data curation via interoperability: use cases with the Medical Imaging and Data Resource Center. ''Sci Data''. 2025 Aug 1; '''12''' (1):1340 | |
| Innovative machine learning approach for liver fibrosis and disease severity evaluation in MAFLD patients using MRI fat content analysis. Description: Hou, Mengting, et al. Innovative machine learning approach for liver fibrosis and disease severity evaluation in MAFLD patients using MRI fat content analysis. ''Clin Exp Med''. 2025 Aug 5; '''25''' (1):275 | |
| A stacking ensemble framework integrating radiomics and deep learning for prognostic prediction in head and neck cancer. Description: Wang, Bingzhen, et al. A stacking ensemble framework integrating radiomics and deep learning for prognostic prediction in head and neck cancer. ''Radiat Oncol''. 2025 Aug 13; '''20''' (1):127 | |
| Research Priorities for Translating Endophenotyping of Adult Obstructive Sleep Apnea to the Clinic: An Official American Thoracic Society Research Statement. Description: Tolbert, Thomas M, et al. Research Priorities for Translating Endophenotyping of Adult Obstructive Sleep Apnea to the Clinic: An Official American Thoracic Society Research Statement. ''Am J Respir Crit Care Med''. 2025 Sep; '''211''' (9):1562-1583 | |
| Uncovering novel functions of NUF2 in glioblastoma and MRI-based expression prediction. Description: Zhong, Rong-de, et al. Uncovering novel functions of NUF2 in glioblastoma and MRI-based expression prediction. ''Sci Rep''. 2025 Sep 1; '''15''' (1):32120 | |
| A pipeline to morph finite element models of the lumbar spine for generation of customised spinal cages. Description: Yu, Yihang, et al. A pipeline to morph finite element models of the lumbar spine for generation of customised spinal cages. ''J Orthop Surg Res''. 2025 Sep 29; '''20''' (1):864 | |
| Survival risk stratification of 2021 WHO glioblastoma by MRI radiomics and biological exploration. Description: Li, Yangyang, et al. Survival risk stratification of 2021 WHO glioblastoma by MRI radiomics and biological exploration. ''BMC Cancer''. 2025 Oct 3; '''25''' (1):1505 | |
| Real-World Benchmarking and Validation of Foundation Model Transformers for Endometrial Cancer Subtyping from Histopathology. Description: Wagner, Vincent M, et al. Real-World Benchmarking and Validation of Foundation Model Transformers for Endometrial Cancer Subtyping from Histopathology. ''medRxiv''. 2025 Oct 13; | |
| RadGLO: an interactive platform for radiomic feature analysis and prognostic modeling in glioma. Description: Kundal, Kavita, et al. RadGLO: an interactive platform for radiomic feature analysis and prognostic modeling in glioma. ''NPJ Precis Oncol''. 2025 Oct 14; '''9''' (1):323 | |
| An integrated MRI-based diagnostic framework for glioma with incomplete imaging sequences and imperfect annotations. Description: Song, Pengfei, et al. An integrated MRI-based diagnostic framework for glioma with incomplete imaging sequences and imperfect annotations. ''NPJ Precis Oncol''. 2025 Oct 23; '''9''' (1):328 | |
| CT radiomic stratification signature to optimize clinical decisions for ovarian cancer patients receiving neoadjuvant chemotherapy and the underlying biological basis: a multicenter retrospective study. Description: Zhang, Shimin, et al. CT radiomic stratification signature to optimize clinical decisions for ovarian cancer patients receiving neoadjuvant chemotherapy and the underlying biological basis: a multicenter retrospective study. ''J Transl Med''. 2025 Oct 28; '''23''' (1):1184 | |
| Radiomic imaging models for predicting breast cancer prognosis based on Interleukin-18 (IL18). Description: Zhou, Qian, et al. Radiomic imaging models for predicting breast cancer prognosis based on Interleukin-18 (IL18). ''BMC Med Imaging''. 2025 Nov 12; '''25''' (1):460 | |
| STPath: a generative foundation model for integrating spatial transcriptomics and whole-slide images. Description: Huang, Tinglin, et al. STPath: a generative foundation model for integrating spatial transcriptomics and whole-slide images. ''NPJ Digit Med''. 2025 Nov 14; '''8''' (1):659 |