Evidence map›Paper›PMID 39358821›Full record

ArticleCancer imaging : the official publication of the International Cancer Imaging Society2024

CT-based conventional radiomics and quantification of intratumoral heterogeneity for predicting benign and malignant renal lesions.

Shuanbao Yu, Yang Yang, Zeyuan Wang, Haoke Zheng, Jinshan Cui, Yonghao Zhan, Junxiao Liu, Peng Li, Yafeng Fan, Wendong Jia and 5 more

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Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 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

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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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

15 authors.

Shuanbao YuDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yang YangDepartment of Information Management, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Zeyuan WangDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Haoke ZhengDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Jinshan CuiDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yonghao ZhanDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Junxiao LiuDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Peng LiDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yafeng FanDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Wendong JiaDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Meng WangDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Bo ChenDepartment of Urology, Tongliao Clinical College, Inner Mongolia Medical University, Tongliao, China.
Jin TaoDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yuhong LiDepartment of Information Management, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Xuepei ZhangDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. zhangxuepei@263.net.ORCID http://orcid.org/0000-0003-0919-5871

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the increasing incidence of renal lesions, pretreatment differentiation between benign and malignant lesions is crucial for optimized management. This study aimed to develop a machine learning model utilizing radiomic features extracted from various regions of interest (ROIs), intratumoral ecological diversity features, and clinical factors to classify renal lesions.

methodsCT images (arterial phase) of 1,795 renal lesions with confirmed pathology from three hospital sites were split into development (1184, 66%) and test (611, 34%) cohorts by surgery date. Conventional radiomic features were extracted from eight ROIs of arterial phase images. Intratumoral ecological diversity features were derived from intratumoral subregions. The combined model incorporating these features with clinical factors was developed, and its performance was compared with radiologists' interpretation.

resultsCombining intratumoral and peritumoral radiomic features, along with ecological diversity features yielded the highest AUC of 0.929 among all combinations of features extracted from CT scans. After incorporating clinical factors into the features extracted from CT images, our combined model outperformed the interpretation of radiologists in the whole (AUC = 0.946 vs 0.823, P < 0.001) and small renal lesion (AUC = 0.935 vs 0.745, P < 0.001) test cohorts. Furthermore, the combined model exhibited favorable concordance and provided the highest net benefit across threshold probabilities exceeding 60%. In the whole and small renal lesion test cohorts, the AUCs for subgroups with predicted risk below or above 95% sensitivity and specificity cutoffs were 0.974 and 0.978, respectively.

conclusionsThe combined model, incorporating intratumoral and peritumoral radiomic features, ecological diversity features, and clinical factors showed good performance for distinguishing benign from malignant renal lesions, surpassing radiologists' diagnoses in both whole and small renal lesions. It has the potential to save patients from unnecessary invasive biopsies/surgeries and to enhance clinical decision-making.

Indexed as

Kidney NeoplasmsTomography, X-Ray ComputedHumansMachine LearningRadiomicsRetrospective StudiesBenign renal lesionComputed tomographyIntratumoral heterogeneityRadiomicsRenal cell carcinomaSmall renal lesion

Identifiers

PMID39358821
PMCPMC11446113

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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.