Evidence map›Paper›PMID 39707027›Full record

ArticleSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2024

Improving the prediction of chemotherapy dose-limiting toxicity in colon cancer patients using an AI-CT-based 3D body composition of the entire L1-L5 lumbar spine.

Ke Cao, Josephine Yeung, Matthew Y K Wei, Cheuk Shan Choi, Margaret Lee, Lincoln J Lim, Yasser Arafat, Paul N Baird, Justin M C Yeung

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Article in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Ke CaoDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia.
Josephine YeungDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia.
Matthew Y K WeiDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia.
Cheuk Shan ChoiDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia.
Margaret LeeDepartment of Oncology, Western Health, Melbourne, Australia.
Lincoln J LimDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia.
Yasser ArafatDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia.
Paul N Baird *Department of Surgery, University of Melbourne, Melbourne, Australia.
Justin M C Yeung *Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia. justin.yeung@unimelb.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeChemotherapy dose-limiting toxicities (DLT) pose a significant challenge in successful colon cancer treatment. Body composition analysis may enable tailored interventions thereby supporting the mitigation of chemotherapy toxic effects. This study aimed to evaluate and compare the effectiveness of using three-dimensional (3D) CT body composition measures from the entire lumbar spine levels (L1-L5) versus a single vertebral level (L3), the current gold standard, in predicting chemotherapy DLT in colon cancer patients.

methodsRetrospective analysis of 184 non-metastatic colon cancer patients receiving adjuvant chemotherapy was performed. DLT was defined as any occurrence of dose reduction or discontinuation due to chemotherapy toxicity. Using artificial intelligence (AI) auto-segmentation, 3D body composition measurements were obtained from patients' L1-L5 levels on CT imaging. The effectiveness of patients' 3D L3 body composition measurement and incorporating data from the entire L1-L5 (including L3) region in predicting DLT was examined.

resultsOf the 184 patients, 112 (60.9%) experienced DLT. Neuropathy was the most common toxicity (49/112, 43.8%) followed by diarrhea (35.7%) and nausea/vomiting (33%). Patients with DLT had lower muscle volume at all lumbar levels compared to those without. The machine learning model incorporating L1-L5 data and patient clinical data achieved high predictive performance (AUC = 0.75, accuracy = 0.75), outperforming the prediction using single L3 level (AUC = 0.65, accuracy = 0.65).

conclusionEvaluating a patient's body composition allowed prediction of chemotherapy toxicities for colon cancer. Incorporating fully automated body composition analysis of CT slices from the entire lumbar region offers promising performance in early identification of high-risk individuals, with the ultimate aim of improving patient's quality of life.

Indexed as

Artificial IntelligenceBody CompositionColonic NeoplasmsLumbar VertebraeTomography, X-Ray ComputedAdultAgedAged, 80 and overAntineoplastic AgentsChemotherapy, AdjuvantDose-Response Relationship, DrugFemaleHumansImaging, Three-DimensionalMaleMiddle AgedAntineoplastic Agents3D body compositionArtificial intelligenceCancer careChemotherapy toxicity

Identifiers

PMID39707027

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