BACKGROUND: Assessing overall tumor burden on the basis of tumor number and size may assist in prognostic stratification of patients after resection of colorectal liver metastases (CRLM). We sought to define the prognostic accuracy of tumor burden by using machine learning (ML) algorithms compared with other commonly used prognostic scoring systems.STUDY DESIGN: Patients who underwent hepatectomy for CRLM between 2001 and 2018 were identified from a multi-institutional database and split into training and validation cohorts. ML was used to define tumor burden (ML-TB) based on CRLM tumor number and size thresholds associated with 5-year overall survival. Prognostic ability of ML-TB was compared with the Fong and Genetic and Morphological Evaluation scores using Cohen's d.RESULTS: Among 1,344 patients who underwent resection of CRLM, median tumor number (2, interquartile range 1 to 3) and size (3 cm, interquartile range 2.0 to 5.0) were comparable in the training (n = 672) vs validation (n = 672) cohorts; patient age (training 60.8 vs validation 61.0) and preoperative CEA (training 10.2 ng/mL vs validation 8.3 ng/mL) was also similar (p > 0.05). ML empirically derived optimal cutoff thresholds for number of lesions (3) and size of the largest lesion (1.3 cm) in the training cohort, which were then used to categorize patients in the validation cohort into 3 prognostic groups. Patients with low, average, or high ML-TB had markedly different 5-year overall survival (51.6%, 40.9%, and 23.1%, respectively; p < 0.001). ML-TB was more effective at stratifying patients relative to 5-year overall survival (low vs high ML-TB, d = 2.73) vs the Fong clinical (d = 1.61) or Genetic and Morphological Evaluation (d = 0.84) scores.CONCLUSIONS: Using a large international cohort, ML was able to stratify patients into 3 distinct prognostic categories based on overall tumor burden. ML-TB was noted to be superior to other CRLM prognostic scoring systems. (c) 2022 by the American College of Surgeons. Published by Wolters Kluwer Health, Inc. All rights reserved.

Machine Learning Approach to Stratifying Prognosis Relative to Tumor Burden after Resection of Colorectal Liver Metastases: An International Cohort Analysis

Guglielmi, Alfredo;Ruzzenente, Andrea;
2022-01-01

Abstract

BACKGROUND: Assessing overall tumor burden on the basis of tumor number and size may assist in prognostic stratification of patients after resection of colorectal liver metastases (CRLM). We sought to define the prognostic accuracy of tumor burden by using machine learning (ML) algorithms compared with other commonly used prognostic scoring systems.STUDY DESIGN: Patients who underwent hepatectomy for CRLM between 2001 and 2018 were identified from a multi-institutional database and split into training and validation cohorts. ML was used to define tumor burden (ML-TB) based on CRLM tumor number and size thresholds associated with 5-year overall survival. Prognostic ability of ML-TB was compared with the Fong and Genetic and Morphological Evaluation scores using Cohen's d.RESULTS: Among 1,344 patients who underwent resection of CRLM, median tumor number (2, interquartile range 1 to 3) and size (3 cm, interquartile range 2.0 to 5.0) were comparable in the training (n = 672) vs validation (n = 672) cohorts; patient age (training 60.8 vs validation 61.0) and preoperative CEA (training 10.2 ng/mL vs validation 8.3 ng/mL) was also similar (p > 0.05). ML empirically derived optimal cutoff thresholds for number of lesions (3) and size of the largest lesion (1.3 cm) in the training cohort, which were then used to categorize patients in the validation cohort into 3 prognostic groups. Patients with low, average, or high ML-TB had markedly different 5-year overall survival (51.6%, 40.9%, and 23.1%, respectively; p < 0.001). ML-TB was more effective at stratifying patients relative to 5-year overall survival (low vs high ML-TB, d = 2.73) vs the Fong clinical (d = 1.61) or Genetic and Morphological Evaluation (d = 0.84) scores.CONCLUSIONS: Using a large international cohort, ML was able to stratify patients into 3 distinct prognostic categories based on overall tumor burden. ML-TB was noted to be superior to other CRLM prognostic scoring systems. (c) 2022 by the American College of Surgeons. Published by Wolters Kluwer Health, Inc. All rights reserved.
Algorithms
Cohort Studies
Follow-Up Studies
Hepatectomy
Humans
Liver Neoplasms
Machine Learning
Middle Aged
Prognosis
Retrospective Studies
Colorectal Neoplasms
Tumor Burden
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1078657
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus ND
  • ???jsp.display-item.citation.isi??? 0
social impact