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Objective To explore the value of constructing a model to predict the number of positive cores in systematic biopsy in prostate cancer (PCa) using a combination of radiomics features based on magnetic resonance imaging and clinical indicators. Methods Retrospectively collected magnetic resonance imaging and clinical data from two medical institutions (Gansu Provincial Hospital from January 2018 to February 2023, Zhangye People's Hospital Affiliated to Hexi College from April 2020 to February 2023). The 155 patients from Gansu Provincial Hospital were randomly divided into a training set (n=109; 80 cases with positive needle count ≥6 and 29 cases with positive needle count <6) and an internal validation set (n=46; 34 cases with positive needle count ≥6 and 12 cases with positive needle count <6) in a 7:3 ratio. The 43 patients from Zhangye People's Hospital Affiliated to Hexi College were used as external validation set. Small field of view high-resolution T2-weighted imaging (sFOV HR-T2WI) and contrast-enhanced delayed-phase images were selected to extract radiomic features from the three-dimensional region of interest of the entire prostate, and radiomics model was constructed and Radscores calculated after dimensionality reduction and feature selection. Univariate and multivariate logistic regressions were used to screen for independent risk factors for positive cores in systematic biopsy. Nomogram was constructed using Radscore and clinical independent risk factors to predict the number of positive cores in systematic biopsy in PCa patients, which was then externally validated. Results Age, alkaline phosphatase (ALP), free prostate specific antigen (FPSA), total prostate specific antigen (TPSA), FPSA/TPSA ratio, and prostate specific antigen density (PSAD) were not statistically significantly different between the training, internal validation, and external validation sets (P>0.05). FPSA, TPSA, FPSA/TPSA ratio, and PSAD were significantly different between the positive cores <6 and positive cores ≥6 groups (P<0.001). Univariate logistic regression analysis showed that FPSA (P<0.001), TPSA (P<0.001), FPSA/TPSA ratio (P=0.001), PSAD (P<0.001), and Radscore (P<0.001) were risk factors for positive cores in systematic biopsy in PCa. Multivariate logistic regression analysis showed that PSAD (OR=0.251, 95%CI 0.063-0.996, P=0.049) and Radscore (OR=1.990, 95%CI 1.409-2.812, P<0.001) were independent risk factors for positive cores in systematic biopsy in PCa. The clinical models achieved AUCs of 0.849(95%CI 0.774-0.924), 0.817(95%CI 0.693-0.941), and 0.631(95%CI 0.439-0.822); the 12 features for radiomics models are derived solely from sFOV HR-T2WI, the radiomics models achieved AUCs of 0.868(95%CI 0.791-0.945), 0.846(95%CI 0.695-0.996), and 0.815(95%CI 0.660-0.970); the nomogram achieved AUCs of 0.921(95%CI 0.869-0.973), 0.868(95%CI 0.743-0.992), and 0.840(95%CI 0.702-0.978) in the training set, internal validation set, and external validation set, respectively. Conclusions The combination of radiomic features extracted from sFOV HR-T2WI and PSAD can preoperatively be used as a noninvasive manner to predict the number of positive cores of the PCa patients. This approach has a certain value in risk stratification of PCa patients and guiding personalized clinical management.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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