Quantifying the Heterogeneity of TRUS-Visible Lesions for Predicting Prostate Cancer: A Multicenter Preliminary Study

Bing Zheng; Jia Zhu; Min Xu; Xia Wu; Jie Jiang; Yong Zhang; Cheng Shen; Zhongxin Zhang; Haiming Huang; Xiang Liu

doi:10.1016/j.acra.2026.03.047

Quantifying the Heterogeneity of TRUS-Visible Lesions for Predicting Prostate Cancer: A Multicenter Preliminary Study

Acad Radiol. 2026 Jul;33(7):2946-2957. doi: 10.1016/j.acra.2026.03.047. Epub 2026 Apr 28.

Authors

Bing Zheng¹, Jia Zhu², Min Xu³, Xia Wu⁴, Jie Jiang¹, Yong Zhang¹, Cheng Shen¹, Zhongxin Zhang³, Haiming Huang³, Xiang Liu⁵

Affiliations

¹ Department of Urology, Nantong First People's Hospital, Nantong, Jiangsu, China (B.Z., J.J., Y.Z., C.S.).
² Department of medical ultrasound, Lianyungang Hospital Affiliated to Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, China (J.Z.).
³ Department of Ultrasound, Nantong First People's Hospital, Nantong, Jiangsu, China (M.X., Z.Z., H.H., X.L.).
⁴ Department of Ultrasound, Rugao Traditional Chinese Medicine Hospital, Rugao, Jiangsu, China (X.W.).
⁵ Department of Ultrasound, Nantong First People's Hospital, Nantong, Jiangsu, China (M.X., Z.Z., H.H., X.L.). Electronic address: ntyylx@163.com.

PMID: 42055868
DOI: 10.1016/j.acra.2026.03.047

Abstract

Rationale and objectives: This study aimed to quantitatively characterize the heterogeneity of Transrectal ultrasound (TRUS)-visible lesions using subregional imaging analysis, and to develop and validate an intratumoral heterogeneity (ITH) model for differentiating prostate cancer (PCa) from benign lesions.

Materials and methods: This retrospective study included patients from three medical centers between June 2022 and April 2025 who underwent TRUS examination prior to biopsy and presented with TRUS-visible lesions. Lesions were partitioned into multiple imaging subregions using K-means clustering, and radiomic features were extracted from both the whole lesion and each subregion. Four predictive models were constructed using multiple machine-learning classifiers: a whole-lesion model (Intratumor) and three subregional models (HabitatH1, HabitatH2, and HabitatH3). An ITH model was developed by integrating the output probabilities of the subregional models. The diagnostic performance of each model was systematically evaluated. In addition, the performance of the ITH model was compared with that of radiologists with different levels of experience.

Results: Among all subregional models, the model integrating HabitatH2 and HabitatH3 demonstrated superior performance and was defined as the ITH model. the ITH model exhibited an AUC of 0.879 (95% CI: 0.808-0.950) in the internal validation set and 0.818 (95%CI: 0.715-0.921) in the external validation set, respectively, outperforming the performance of both the Intratumor and clinical models. The ITH model demonstrated superior diagnostic performance compared to junior radiologists (p<0.05) and significantly enhanced the diagnostic accuracy of the junior radiologists (p<0.05).

Conclusions: This study developed an ITH model to quantify the heterogeneity of TRUS-visible lesions, demonstrating potential predictive value for PCa.

Keywords: Heterogeneity; Prostate cancer; Radiomics; Subregional imaging; Ultrasound.