Artificial Intelligence-Based Prediction of Recurrence after Curative Resection for Colorectal Cancer from Digital Pathological Images

Ryota Nakanishi, Ken’ichi Morooka, Kazuki Omori, Satoshi Toyota, Yasushi Tanaka, Hirofumi Hasuda, Naomichi Koga, Kentaro Nonaka, Qingjiang Hu, Yu Nakaji, Tomonori Nakanoko, Koji Ando, Mitsuhiko Ota, Yasue Kimura, Eiji Oki, Yoshinao Oda, Tomoharu Yoshizumi

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Background: To develop an artificial intelligence-based model to predict recurrence after curative resection for stage I–III colorectal cancer from digitized pathological slides. Patients and Methods: In 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. Results: The 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). Conclusions: The 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.

Original languageEnglish
Pages (from-to)3506-3514
Number of pages9
JournalAnnals of Surgical Oncology
Volume30
Issue number6
DOIs
Publication statusPublished - Jun 2023

All Science Journal Classification (ASJC) codes

  • Surgery
  • Oncology

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