Evidence map›Paper›PMID 38740661›Full record

ArticleJournal of imaging informatics in medicine2025

Deep Learning-Based Detect-Then-Track Pipeline for Treatment Outcome Assessments in Immunotherapy-Treated Liver Cancer.

Jie Zhou, Yujia Xia, Xiaolei Xun, Zhangsheng Yu

Abstract read
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In one paragraph

Article in Journal of imaging informatics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

4 authors.

Jie Zhou *Department of Statistics, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China.
Yujia Xia *Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Xiaolei XunGlobal Statistics and Data Science, BeiGene, Shanghai, China.
Zhangsheng YuDepartment of Statistics, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China. yuzhangsheng@sjtu.edu.cn.ORCID http://orcid.org/0000-0002-8189-5330

Funding

Collaboration of SJTU-Beigene 21H010104287Medical Engineering Cross Fund of Shanghai Jiao Tong University YG2023ZD21National Natural Science Foundation of China 12171318Science and Technology Innovation Plan Of Shanghai Science and Technology Commission 21ZR1436300Shanghai Jiao Tong University STAR Grant 20190102
6 · The paper itself

Abstract

Accurate treatment outcome assessment is crucial in clinical trials. However, due to the image-reading subjectivity, there exist discrepancies among different radiologists. The situation is common in liver cancer due to the complexity of abdominal scans and the heterogeneity of radiological imaging manifestations in liver subtypes. Therefore, we developed a deep learning-based detect-then-track pipeline that can automatically identify liver lesions from 3D CT scans then longitudinally track target lesions, thereby providing the evaluation of RECIST treatment outcomes in liver cancer. We constructed and validated the pipeline on 173 multi-national patients (344 venous-phase CT scans) consisting of a public dataset and two in-house cohorts of 28 centers. The proposed pipeline achieved a mean average precision of 0.806 and 0.726 of lesion detection on the validation and test sets. The model's diameter measurement reliability and consistency are significantly higher than that of clinicians (p = 1.6 × 10

Indexed as

Deep LearningImmunotherapyLiver NeoplasmsTomography, X-Ray ComputedAgedFemaleHumansMaleMiddle AgedReproducibility of ResultsResponse Evaluation Criteria in Solid TumorsTreatment OutcomeDeep learningLesion detectionLiver cancerLongitudinal CTsOncologic response

Identifiers

PMID38740661
PMCPMC11811366

What OpenQuestion 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 OpenQuestion graph.