The potential of prostate gland radiomic features in identifying the Gleason score

Lixin Gong; Min Xu; Mengjie Fang; Bingxi He; Hailin Li; Xiangming Fang; Di Dong; Jie Tian

doi:10.1016/j.compbiomed.2022.105318

The potential of prostate gland radiomic features in identifying the Gleason score

Comput Biol Med. 2022 May:144:105318. doi: 10.1016/j.compbiomed.2022.105318. Epub 2022 Feb 11.

Authors

Lixin Gong¹, Min Xu², Mengjie Fang³, Bingxi He⁴, Hailin Li⁴, Xiangming Fang⁵, Di Dong⁶, Jie Tian⁷

Affiliations

¹ College of Medicine and Biological Information Engineering School, Northeastern University, Shenyang, 110016, China; CAS Key Laboratory of Molecular Imaging, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
² Imaging Center, Wuxi People's Hospital, Nanjing Medical University, Wuxi, 214023, China.
³ CAS Key Laboratory of Molecular Imaging, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
⁴ CAS Key Laboratory of Molecular Imaging, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, China.
⁵ Imaging Center, Wuxi People's Hospital, Nanjing Medical University, Wuxi, 214023, China. Electronic address: [email protected].
⁶ CAS Key Laboratory of Molecular Imaging, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China. Electronic address: [email protected].
⁷ College of Medicine and Biological Information Engineering School, Northeastern University, Shenyang, 110016, China; CAS Key Laboratory of Molecular Imaging, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, China; Zhuhai Precision Medical Center, Zhuhai People's Hospital (affiliated with Jinan University), Zhuhai, 519000, China. Electronic address: [email protected].

PMID: 35245698
DOI: 10.1016/j.compbiomed.2022.105318

Abstract

Background: Gleason score (GS) is one of the most critical predictors of diagnosing prostate cancer (PCa). The prostate gland, including both lesions and their microenvironment, may contain more comprehensive information about the PCa. We aimed to investigate the potential of prostate gland radiomic features in identifying Gleason scores (GS) < 7, = 7, and >7.

Methods: We retrospectively examined preoperative magnetic resonance imaging (MRI) results, clinical data, and postoperative pathological findings from 489 PCa patients. The three-dimensional (3D) and two-dimensional (2D) radiomic features were extracted from the manually segmented 3D prostate gland and its maximum 2D layer on MRI, respectively. Significant features were selected, and sequence signatures were then developed via multi-class linear regression (MLR) accordingly. Subsequently, 2D and 3D radiomic models were constructed by applying MLR to the combination of the sequence signatures, respectively. The stability of the significant features was discussed by their average ranking in the other 30 random cohorts. Based on our distance matrix algorithm, we generated different regions of interest to simulate the manual segmentation biases and discuss the model's tolerance to them.

Results: Our 2D model reached a C-index of 0.728 and an average area under the receiver operating characteristic curve of 0.794 in the validation cohort. The corresponding key features were stable, with an average ranking of the top 8.352% in 30 random cohorts, and the model could tolerate a segmentation boundary deviation of 2 mm without significant performance degradation.

Conclusion: 2D prostate-gland-MRI-based radiomic features showed stable potential in identifying GS.

Keywords: Gland segmentation; Gleason score; MRI; Prostate cancer; Radiomics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Humans
Magnetic Resonance Imaging / methods
Male
Neoplasm Grading
Prostate* / diagnostic imaging
Prostatic Neoplasms* / diagnostic imaging
Prostatic Neoplasms* / pathology
Retrospective Studies
Tumor Microenvironment