Evidence map›Paper›PMID 37189155›Full record

ReviewMilitary Medical Research2023

Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.

Yuan-Peng Zhang, Xin-Yun Zhang, Yu-Ting Cheng, Bing Li, Xin-Zhi Teng, Jiang Zhang, Saikit Lam, Ta Zhou, Zong-Rui Ma, Jia-Bao Sheng and 4 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Military Medical Research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 137 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
137citing papers in PubMed, 10 pooled it
–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

137 citing papers in PubMed, 10 syntheses or guidelines pooled it.

  1. Precision or Paradox? AI-Driven Adiposity Imaging in Women With Overweight and Obesity: A Systematic Review and Meta-Analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026
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  10. Diagnostic Value ofTechnology in cancer research & treatment
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  11. Intratumor habitat aware deep learning framework for preoperative lymph node metastasis prediction in laryngeal carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
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77 more citing papers are in PubMed but not listed here.

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

14 authors.

Yuan-Peng Zhang *Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China.
Xin-Yun Zhang *Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China.
Yu-Ting Cheng *Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China.
Bing Li *Department of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China.
Xin-Zhi TengDepartment of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China.
Jiang ZhangDepartment of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China.
Saikit LamDepartment of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China.
Ta ZhouDepartment of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China.
Zong-Rui MaDepartment of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China.
Jia-Bao ShengDepartment of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China.
Victor C W TamDepartment of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China.
Shara W Y LeeDepartment of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China.
Hong GeDepartment of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China.
Jing CaiDepartment of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China. jing.cai@polyu.edu.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients' anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some potentially useful quantitative information in medical images, especially that which is not visible to the naked eye, is often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from medical images, which enables quantitative analysis of medical images and prediction of various clinical endpoints. Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis, demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine. However, radiomics remains in a developmental phase as numerous technical challenges have yet to be solved, especially in feature engineering and statistical modeling. In this review, we introduce the current utility of radiomics by summarizing research on its application in the diagnosis, prognosis, and prediction of treatment responses in patients with cancer. We focus on machine learning approaches, for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling. Furthermore, we introduce the stability, reproducibility, and interpretability of features, and the generalizability and interpretability of models. Finally, we offer possible solutions to current challenges in radiomics research.

Indexed as

Artificial IntelligenceNeoplasmsHumansMachine LearningPrognosisReproducibility of ResultsArtificial intelligenceFeature extractionFeature selectionHead and neck cancerInterpretabilityModelingMulti-modalitiesRadiomics

Identifiers

PMID37189155
PMCPMC10186733

What OpenQuestion holds

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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.