ArticleJournal of hematology & oncology2022
Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients.
Article in Journal of hematology & oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 171 papers, 2 of them syntheses that pooled it.
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Who cites it
171 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Deep learning or radiomics based on CT for predicting the response of gastric cancer to neoadjuvant chemotherapy: a meta-analysis and systematic review.Frontiers in oncology · 2024Pooled it
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- Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer.Journal of cancer research and clinical oncology · 2024Trial
- Development and validation of a prognostic scoring system for patients with colorectal cancer hepato-pulmonary metastasis: a retrospective study.BMC cancer · 2022Trial
- The Differential Diagnostic Value of Radiomics Signatures Between Single-Nodule Pulmonary Metastases and Second Primary Lung Cancer in Patients with Colorectal Cancer.Technology in cancer research & treatmentTrial
- Role of Surgical Therapy in Pulmonary Mucosa-Associated Lymphoid Tissue Lymphoma and Development of a Survival Nomogram.Annals of surgical oncology · 2026Article
- Multimodal radiopathomics model predicts postoperative metachronous liver metastasis in gastric cancer.Nature communications · 2026Article
- Development and Validation of an Interpretable Machine Learning Model for Predicting Early Pulmonary Metastasis Risk in Osteosarcoma.Cancer medicine · 2026Article
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- Dual modal pathomics model for colorectal cancer early recurrence prediction and mutation landscape analysis.iScience · 2026Article
- A Multimodal, Multitask Prediction Framework for Diagnosis and Prognosis of Clear Cell Renal Cell Carcinoma.Journal of imaging informatics in medicine · 2026Article
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- A radio-pathological fusion model for predicting PD-L1 expression and immunotherapy response in non-small cell lung cancer.Insights into imaging · 2026Article
- Development and validation of a radiomics-dosiomics model for predicting radiotherapy response in locally advanced, unresectable non-small cell lung cancer: a multi-center study.Scientific reports · 2026Article
- Artificial intelligence for the prediction of prognosis in colorectal cancer patients using routine blood indices.NPJ digital medicine · 2026Article
- Application of SHAP visualization and nomogram based on PET/CT radiomics combined models for risk stratification in high-grade invasive lung adenocarcinoma.Journal of translational medicine · 2026Article
- Pleural fluid C-C motif chemokine ligand 2 as a diagnostic biomarker for malignant pleural effusion.Journal of thoracic disease · 2026Article
- A multimodal approach integrating NK cell-associated gene signatures and pathomics to predict colon adenocarcinoma prognosis.Scientific reports · 2026Article
- Unlocking the prognostic power of pathomics in bladder cancer: a machine learning odyssey across multiple centers.BMC medical imaging · 2026Article
111 more citing papers are in PubMed but not listed here.
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11 authors.
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Abstract
Limited previous studies focused on the death and progression risk stratification of colorectal cancer (CRC) lung metastasis patients. The aim of this study is to construct a nomogram model combing machine learning-pathomics, radiomics features, Immunoscore and clinical factors to predict the postoperative outcome of CRC patients with lung metastasis. In this study, a total of 103 CRC patients having metastases limited to lung and undergoing radical lung resection were identified. Patch-level convolutional neural network training in weakly supervised manner was used to perform whole slides histopathological images survival analysis. Synthetic minority oversampling technique and support vector machine classifier were used to identify radiomics features and build predictive signature. The Immunoscore for each patient was calculated from the density of CD3+ and CD8+ cells at the invasive margin and the center of metastatic tumor which were assessed on consecutive sections of automated digital pathology. Finally, pathomics and radiomics signatures were successfully developed to predict the overall survival (OS) and disease free survival (DFS) of patients. The predicted pathomics and radiomics scores are negatively correlated with Immunoscore and they are three independent prognostic factors for OS and DFS prediction. The combined nomogram showed outstanding performance in predicting OS (AUC = 0.860) and DFS (AUC = 0.875). The calibration curve and decision curve analysis demonstrated the considerable clinical usefulness of the combined nomogram. Taken together, the developed nomogram model consisting of machine learning-pathomics signature, radiomics signature, Immunoscore and clinical features could be reliable in predicting postoperative OS and DFS of colorectal lung metastasis patients.
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