Evidence map›Paper›PMID 37540253›Full record

ArticleJournal of cancer research and clinical oncology2023

Can AI-based body composition assessment outperform body surface area in predicting dose-limiting toxicities for colonic cancer patients on chemotherapy?

Ke Cao, Josephine Yeung, Yasser Arafat, CheukShan Choi, Matthew Y K Wei, Steven Chan, Margaret Lee, Paul N Baird, Justin M C Yeung

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.8field-weighted citation impact, top 15% of its field
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

8 citing papers in PubMed, 10 citations in OpenAlex.

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  6. 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.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024
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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

9 authors at 2 institutions in 1 country.

Ke CaoDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia.
Josephine YeungDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia.
Yasser ArafatDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia.
CheukShan ChoiDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia.
Matthew Y K WeiDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia.
Steven ChanDepartment of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia.
Margaret LeeDepartment of Oncology, Western Health, 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.
University of Melbourne · AUWestern Health · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeGold standard chemotherapy dosage is based on body surface area (BSA); however many patients experience dose-limiting toxicities (DLT). We aimed to evaluate the effectiveness of BSA, two-dimensional (2D) and three-dimensional (3D) body composition (BC) measurements derived from Lumbar 3 vertebra (L3) computed tomography (CT) slices, in predicting DLT in colon cancer patients.

methods203 patients (60.87 ± 12.42 years; 97 males, 47.8%) receiving adjuvant chemotherapy (Oxaliplatin and/or 5-Fluorouracil) were retrospectively evaluated. An artificial intelligence segmentation model was used to extract 2D and 3D body composition measurements from each patients' single mid-L3 CT slice as well as multiple-L3 CT scans to produce a 3D BC report. DLT was defined as any incidence of dose reduction or discontinuation due to chemotherapy toxicities. A receiver operating characteristic (ROC) analysis was performed on BSA and individual body composition measurements to demonstrate their predictive performance.

resultsA total of 120 (59.1%) patients experienced DLT. Age and BSA did not vary significantly between DLT and non-DLT group. Females were significantly more likely to experience DLT (p = 4.9 × 10

conclusionCompared to BSA and 2D body composition measurements, 3D L3 body composition measurements had greater potential to predict DLT in CRC patients receiving chemotherapy and this was sex dependent.

Indexed as

Antineoplastic Combined Chemotherapy ProtocolsArtificial IntelligenceBody CompositionBody Surface AreaColonic NeoplasmsAdultAgedChemotherapy, AdjuvantFemaleFluorouracilHumansMaleMiddle AgedOxaliplatinRetrospective StudiesTomography, X-Ray ComputedFluorouracilOxaliplatinArtificial intelligenceBody compositionChemotherapy toxicityClinical oncologyComputed tomography

Identifiers

PMID37540253
PMCPMC10590342
OpenAlexW4385563474

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.