Evidence map›Paper›PMID 40095334›Full record

ArticleInternational journal of clinical oncology2025

Utility of comprehensive genomic profiling combined with machine learning for prognostic stratification in stage II/III colorectal cancer after adjuvant chemotherapy.

Yosuke Kobayashi, Yoshiyuki Suzuki, Ryo Seishima, Yuko Chikaishi, Hiroshi Matsuoka, Kohei Nakamura, Kohei Shigeta, Koji Okabayashi, Junichiro Hiro, Koki Otsuka and 5 more

Abstract read
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In one paragraph

Article in International journal of clinical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Yosuke KobayashiDepartment of Surgery, Fujita Health University, 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi, 470-1192, Japan.
Yoshiyuki SuzukiMatsushima Hospital, Kanagawa, 220-0041, Japan.
Ryo SeishimaDepartment of Surgery, Fujita Health University, 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi, 470-1192, Japan. ryo.seishima@fujita-hu.ac.jp.
Yuko ChikaishiDepartment of Surgery, Fujita Health University, 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi, 470-1192, Japan.
Hiroshi MatsuokaDepartment of Surgery, Fujita Health University, 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi, 470-1192, Japan.
Kohei NakamuraCenter for Cancer Genomics, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Kohei ShigetaDepartment of Surgery, Keio University School of Medicine, Tokyo, 160‑8582, Japan.
Koji OkabayashiDepartment of Surgery, Keio University School of Medicine, Tokyo, 160‑8582, Japan.
Junichiro HiroDepartment of Surgery, Fujita Health University, 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi, 470-1192, Japan.
Koki OtsukaDepartment of Advanced Robotic and Endoscopic Surgery, Fujita Health University, Toyoake, Aichi, 470-1192, Japan.
Ichiro UyamaDepartment of Advanced Robotic and Endoscopic Surgery, Fujita Health University, Toyoake, Aichi, 470-1192, Japan.
Hideyuki SayaDivision of Gene Regulation, Oncology Innovation Center, Fujita Health University, Toyoake, Aichi, 470-1192, Japan.
Hiroshi NishiharaCenter for Cancer Genomics, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Koichi SudaDepartment of Surgery, Fujita Health University, 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi, 470-1192, Japan.
Yuko KitagawaDepartment of Surgery, Keio University School of Medicine, Tokyo, 160‑8582, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposeAccurate recurrence risk evaluation in patients with stage II and III colorectal cancer (CRC) remains difficult. Traditional histopathological methods frequently fall short in predicting outcomes after adjuvant chemotherapy. This study aims to evaluate the use of comprehensive genomic profiling combined with machine learning for prognostic risk stratification in patients with CRC.

methodsA machine learning model was developed using a training cohort of 52 patients with stage II/III CRC who underwent curative surgery at Fujita Health University Hospital. Genomic DNA was isolated from formalin-fixed, paraffin-embedded tissue sections and analyzed with a 160 cancer-related gene panel. The random forest algorithm was used to determine key genes affecting recurrence-free survival. The model was validated by developing a risk score with internal and external cohorts, including 44 patients from Keio University Hospital.

resultsSix key genes (KRAS, KIT, SMAD4, ARID2, NF1, and FBXW7) were determined as significant prognostic risk predictors. A risk score system integrating these genes with clinicopathological factors effectively stratified patients in both internal (p < 0.001) and external cohorts (p = 0.017).

conclusionsThis study reveals that machine learning, combined with comprehensive genomic profiling, significantly improves prognostic risk stratification in patients with stage II/III CRC after adjuvant chemotherapy. This approach provides a promising tool for individualized treatment strategies, warranting further validation with larger cohorts.

Indexed as

Colorectal NeoplasmsGene Expression ProfilingMachine LearningAdultAgedBiomarkers, TumorChemotherapy, AdjuvantFemaleHumansMaleMiddle AgedNeoplasm Recurrence, LocalNeoplasm StagingPrognosisBiomarkers, TumorColorectal neoplasmsGenome testingMachine learning

Identifiers

PMID40095334

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.