Predicting risk of recurrence after resection of stage I intrahepatic cholangiocarcinoma

Kawashima, Jun; Sahara, Kota; Shen, Feng; Guglielmi, Alfredo; Aldrighetti, Luca; Weiss, Matthew; Bauer, Todd W.; Alexandrescu, Sorin; Poultsides, George A.; Maithel, Shishir K.; Marques, Hugo P.; Martel, Guillaume; Pulitano, Carlo; Cauchy, François; Koerkamp, B. Groot; Matsuyama, Ryusei; Endo, Itaru; Pawlik, Timothy M.

doi:10.1016/j.gassur.2023.10.002

Journal of Gastrointestinal Surgery

2024

DOI: 10.1016/j.gassur.2023.10.002

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Predicting risk of recurrence after resection of stage I intrahepatic cholangiocarcinoma

Jun Kawashima,

Kota Sahara,

Feng Shen

et al.

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2024

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Cited by 4 publications

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Preoperative identification of early extrahepatic recurrence after hepatectomy for colorectal liver metastases: A machine learning approach

Kawashima,

Endo,

Woldesenbet

et al. 2024

World j. surg.

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BackgroundMachine learning (ML) may provide novel insights into data patterns and improve model prediction accuracy. The current study sought to develop and validate an ML model to predict early extra‐hepatic recurrence (EEHR) among patients undergoing resection of colorectal liver metastasis (CRLM).MethodsPatients with CRLM who underwent curative‐intent resection between 2000 and 2020 were identified from an international multi‐institutional database. An eXtreme gradient boosting (XGBoost) model was developed to estimate the risk of EEHR, defined as extrahepatic recurrence within 12 months after hepatectomy, using clinicopathological factors. The relative importance of factors was determined using Shapley additive explanations (SHAP) values.ResultsAmong 1410 patients undergoing curative‐intent resection, 131 (9.3%) patients experienced EEHR. Median OS among patients with and without EEHR was 35.4 months (interquartile range [IQR] 29.9–46.7) versus 120.5 months (IQR 97.2–134.0), respectively (p < 0.001). The ML predictive model had c‐index values of 0.77 (95% CI, 0.72–0.81) and 0.77 (95% CI, 0.73–0.80) in the entire dataset and the validation data set with bootstrapping resamples, respectively. The SHAP algorithm demonstrated that T and N primary tumor categories, as well as tumor burden score were the three most important predictors of EEHR. An easy‐to‐use risk calculator for EEHR was developed and made available online at: https://junkawashima.shinyapps.io/EEHR/.ConclusionsAn easy‐to‐use online calculator was developed using ML to help clinicians predict the chance of EEHR after curative‐intent resection for CRLM. This tool may help clinicians in decision‐making related to treatment strategies for patients with CRLM.

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Preoperative identification of early extrahepatic recurrence after hepatectomy for colorectal liver metastases: A machine learning approach

Kawashima,

Endo,

Woldesenbet

et al. 2024

World j. surg.

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Unraveling early recurrence of risk factors in Gallbladder cancer: A systematic review and meta-analysis

Yang,

Feng,

Tian

et al. 2024

European Journal of Surgical Oncology

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Gallengangskarzinome – up to date in Diagnostik und Therapie

Perkhofer,

Schütz

2024

Gastroenterologie up2date

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Predicting risk of recurrence after resection of stage I intrahepatic cholangiocarcinoma

Cited by 4 publications

References 36 publications

Preoperative identification of early extrahepatic recurrence after hepatectomy for colorectal liver metastases: A machine learning approach

Preoperative identification of early extrahepatic recurrence after hepatectomy for colorectal liver metastases: A machine learning approach

Unraveling early recurrence of risk factors in Gallbladder cancer: A systematic review and meta-analysis

Gallengangskarzinome – up to date in Diagnostik und Therapie

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