ArticleAnnals of surgical oncology2023
Artificial Intelligence-Based Prediction of Recurrence after Curative Resection for Colorectal Cancer from Digital Pathological Images.
Article in Annals of surgical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 14 citations in OpenAlex.
- Can machine learning predict vessels that encapsulates tumor cluster ptterns and histological differentiation in solitary small (< 5 cm) hepatocellular carcinoma?Surgery today · 2026Article
- Article
- Pre- and post-diagnostic meat intake in relation to risk of recurrence and mortality among individuals with stage I-III colorectal cancer.International journal of cancer · 2026Article
- Review
- Personalized Medical Approach in Gastrointestinal Surgical Oncology: Current Trends and Future Perspectives.Journal of personalized medicine · 2025Review
- Towards understanding cancer dormancy over strategic hitching up mechanisms to technologies.Molecular cancer · 2025Review
- The Impact of Artificial Intelligence on Cancer Diagnosis and Treatment: A Review.Cancer informatics · 2025Review
- Whole slide image based prognosis prediction in rectal cancer using unsupervised artificial intelligence.BMC cancer · 2024Article
- Machine learning based prediction of recurrence after curative resection for rectal cancer.PloS one · 2023Article
Corrections and comments
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Authors and funding
17 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo develop an artificial intelligence-based model to predict recurrence after curative resection for stage I-III colorectal cancer from digitized pathological slides. PATIENTS AND
methodsIn this retrospective study, 471 consecutive patients who underwent curative resection for stage I-III colorectal cancer at our institution from 2004 to 2015 were enrolled, and 512 randomly selected tiles from digitally scanned images of hematoxylin and eosin-stained tumor tissue sections were used to train a convolutional neural network. Five-fold cross-validation was used to validate the model. The association between recurrence and the model's output scores were analyzed in the test cohorts.
resultsThe area under the receiver operating characteristic curve of the cross-validation was 0.7245 [95% confidence interval (CI) 0.6707-0.7783; P < 0.0001]. The score successfully classified patients into those with better and worse recurrence free survival (P < 0.0001). Multivariate analysis revealed that a high score was significantly associated with worse recurrence free survival [odds ratio (OR) 1.857; 95% CI 1.248-2.805; P = 0.0021], which was independent from other predictive factors: male sex (P = 0.0238), rectal cancer (P = 0.0396), preoperative abnormal carcinoembryonic antigen (CEA) level (P = 0.0216), pathological T3/T4 stage (P = 0.0162), and pathological positive lymph node metastasis (P < 0.0001).
conclusionsThe artificial intelligence-based prediction model discriminated patients with a high risk of recurrence. This approach could help decision-makers consider the benefits of adjuvant chemotherapy.
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