Gong Zijian, Liu Zhixuan, Huang Kaiyao, Zou Jie, Wu Zijing, Peng Yun, Ying Hongxing, Gong Lianggeng, Xiang Xiaochang, Ye Yinquan
Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Jiangxi Provincial Key Laboratory of Intelligent Medical Imaging, Nanchang, China.
Quant Imaging Med Surg. 2025 Sep 1;15(9):8395-8408. doi: 10.21037/qims-2025-223. Epub 2025 Aug 15.
The application of habitat analysis is anticipated to enhance the diagnostic efficacy of magnetic resonance imaging (MRI) in prostate cancer (PCa) by providing a more accurate reflection of the microenvironmental characteristics within the lesion. The objective of this study was to investigate the feasibility of multisequence and multiregional MRI-based habitat analysis in the differentiation of PCa and benign prostatic hyperplasia (BPH).
We retrospectively evaluated the data of 673 cases from The Second Affiliated Hospital of Nanchang University and The First Hospital of Xiushui who received MRI examination of the prostate and pathologically confirmed diagnosis of PCa or BPH. Habitat features and classical radiomic features from the regions of lesions and prostate gland (PG) were extracted for model construction. Receiver operating characteristic analysis was used to assess the performance of the models. An integrated nomogram combining dominant models and clinical variables was ultimately constructed. In addition, we further assessed the performance of the nomogram in a subgroup of early-stage lesions without capsular invasion (CIV). The Delong test was used to compare the differences in the area under receiver operating characteristic curve (AUC) between models.
The AUCs of the habitat radiomics score (rad-score) based on the lesion (LHrad-score) in both the internal (0.898) and external validation (0.878) sets were higher than those of the rad-score based on the lesion (0.860 and 0.854, respectively). The AUCs of the classical rad-score based on PG (PCrad-score; 0.883 and 0.865 in the internal and external sets, respectively) were higher than those of the habitat rad-score based on PG (0.871 and 0.773, respectively). By combining the PCrad-score and LHrad-score with clinically independent predictors, the nomogram yielded AUCs of 0.899 and 0.963 in the internal and external sets, respectively. Discrimination between early-stage PCa and BPH in the overall validation set yielded an AUC of 0.802.
The habitat analysis may serve as a means to noninvasively and preoperatively identifying PCa from BPH, even in the early stages of PCa.
通过更准确地反映病变内的微环境特征，预计栖息地分析的应用可提高磁共振成像（MRI）对前列腺癌（PCa）的诊断效能。本研究的目的是探讨基于多序列和多区域MRI的栖息地分析在鉴别PCa和良性前列腺增生（BPH）中的可行性。
我们回顾性评估了南昌大学第二附属医院和修水县第一医院673例接受前列腺MRI检查并经病理确诊为PCa或BPH的病例数据。从病变区域和前列腺（PG）提取栖息地特征和经典影像组学特征用于模型构建。采用受试者操作特征分析来评估模型的性能。最终构建了一个结合主导模型和临床变量的综合列线图。此外，我们进一步评估了列线图在无包膜侵犯（CIV）的早期病变亚组中的性能。使用德龙检验比较模型之间受试者操作特征曲线下面积（AUC）的差异。
基于病变的栖息地影像组学评分（rad-score，LHrad-score）在内部验证集（0.898）和外部验证集（0.878）中的AUC均高于基于病变的rad-score（分别为0.860和0.854）。基于PG的经典rad-score（PCrad-score；内部和外部集分别为0.883和0.865）的AUC高于基于PG的栖息地rad-score（分别为为0.871和0.773）。通过将PCrad-score和LHrad-score与临床独立预测因素相结合，列线图在内部和外部集中的AUC分别为0.899和0.963。在总体验证集中，早期PCa和BPH之间的鉴别AUC为0.802。
栖息地分析可作为一种在术前非侵入性地从BPH中识别PCa的方法，即使在PCa的早期阶段也是如此。
