Evidence map›Paper›PMID 39643905›Full record

ArticleBMC cancer2024

Radiomics for differential diagnosis of Bosniak II-IV renal masses via CT imaging.

Xun Zhao, Ye Yan, Wanfang Xie, Zijian Qin, Litao Zhao, Cheng Liu, Shudong Zhang, Jiangang Liu, Lulin Ma

Abstract read
In one paragraph

Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Prediction of neoadjuvant chemotherapy efficacy in patients with HER2-low breast cancer based on ultrasound radiomics.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025
    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

9 authors.

Xun Zhao *Department of Urology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, 100191, P.R. China.
Ye Yan *Department of Urology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, 100191, P.R. China.
Wanfang Xie *School of Engineering Medicine, Beihang University, Beijing, 100191, P.R. China.
Zijian QinDepartment of Urology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, 100191, P.R. China.
Litao ZhaoSchool of Engineering Medicine, Beihang University, Beijing, 100191, P.R. China.
Cheng LiuDepartment of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hongkou District, Shanghai, P.R. China.
Shudong ZhangDepartment of Urology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, 100191, P.R. China. shootong@163.com.
Jiangang LiuSchool of Engineering Medicine, Beihang University, Beijing, 100191, P.R. China. jgliu@buaa.edu.cn.
Lulin MaDepartment of Urology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, 100191, P.R. China. malulinpku@163.com.

Funding

National Nature Science Foundation of China 82173385National Science Foundation of Beijing Z200027
6 · The paper itself

Abstract

RATIONALE AND

objectivesThe management of complex renal cysts is guided by the Bosniak classification system, which may be inadequate for risk stratification of patients to determine the appropriate intervention. Radiomics models based on CT imaging may provide additional useful information. MATERIALS AND

methodsA total of 322 patients with Bosniak II-IV cysts were included in the study from January 2010 to December 2019. Contrast-enhanced CT scans were performed on all patients. ITK-snap was used for segmentation, and the PyRadiomics 3.0.1 package was used for feature extraction. The radiomics features were screened via the least absolute shrinkage and selection operator (LASSO) regression method. After feature selection, a logistic regression (LR) model, support vector machine (SVM) model and random forest (RF) model were constructed.

resultsIn the present study, 217 benign renal cysts (67.4%) and 105 cystic renal cell carcinomas (32.6%) were identified. According to the Bosniak classification, the sample included 179 (55.6%) Bosniak II cysts, 38 (11.8%) Bosniak IIF cysts, 44 (13.7%) Bosniak III cysts and 61 (18.9%) Bosniak IV cysts. A total of 1334 radiomics features were extracted from both unenhanced and cortical CT scans. After LASSO regression, all the models (LR, SVM and RF) showed satisfactory discrimination and reliability in both unenhanced and cortical CT scans (AUC > 0.950). In the Bosniak IIF-III subgroup analysis, the diagnostic accuracy of the LR model was very low for both the unenhanced and cortical scans. In contrast, the SVM model and RF model showed excellent and stable performance in classifying Bosniak IIF-III cysts. The AUCs of the models were all > 0.85, with a maximum of 0.941. The sensitivity, specificity, accuracy, and AUC of the RF model were 0.889, 0.913, 0.902, and 0.941, respectively.

conclusionOur data indicate that radiomics models can effectively distinguish between cystic renal cell carcinoma (cRCC) and complex renal cysts (Bosniak II-IV). Radiomics models may still have high diagnostic accuracy even for Bosniak IIF-III cysts that are clinically difficult to distinguish. However, external validation of these findings is still needed.

Indexed as

Carcinoma, Renal CellKidney Diseases, CysticKidney NeoplasmsSupport Vector MachineTomography, X-Ray ComputedAdultAgedDiagnosis, DifferentialFemaleHumansMaleMiddle AgedRadiomicsRetrospective StudiesCystic renal cell carcinomaDifferential diagnosisRadiomicsRenal cysts

Identifiers

PMID39643905
PMCPMC11622457

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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.