Algorithms for classification of sequences and segmentation of prostate gland: an external validation study.

OBJECTIVES: The aim of the study was to externally validate two AI models for the classification of prostate mpMRI sequences and segmentation of the prostate gland on T2WI. MATERIALS AND METHODS: MpMRI data from 719 patients were retrospectively collected from two hospitals, utilizing nine MR scanners from four different vendors, over the period from February 2018 to May 2022. Med3D deep learning pretrained architecture was used to perform image classification,UNet-3D was used to segment the prostate gland. The images were classified into one of nine image types by the mode. The segmentation model was validated using T2WI images. The accuracy of the segmentation was evaluated by measuring the DSC, VS,AHD.Finally,efficacy of the models was compared for different MR field strengths and sequences. RESULTS: 20,551 image groups were obtained from 719 MR studies. The classification model accuracy is 99%, with a kappa of 0.932. The precision, recall, and F1 values for the nine image types had statistically significant differences, respectively (all P < 0.001). The accuracy for scanners 1.436 T, 1.5 T, and 3.0 T was 87%, 86%, and 98%, respectively (P < 0.001). For segmentation model, the median DSC was 0.942 to 0.955, the median VS was 0.974 to 0.982, and the median AHD was 5.55 to 6.49 mm,respectively.These values also had statistically significant differences for the three different magnetic field strengths (all P < 0.001). CONCLUSION: The AI models for mpMRI image classification and prostate segmentation demonstrated good performance during external validation, which could enhance efficiency in prostate volume measurement and cancer detection with mpMRI. CLINICAL RELEVANCE STATEMENT: These models can greatly improve the work efficiency in cancer detection, measurement of prostate volume and guided biopsies.

Using an artificial intelligence model to detect and localize visible clinically significant prostate cancer in prostate magnetic resonance imaging: a multicenter external validation study.

Background: An increasing number of patients with suspected clinically significant prostate cancer (csPCa) are undergoing prostate multiparametric magnetic resonance imaging (mpMRI). The role of artificial intelligence (AI) algorithms in interpreting prostate mpMRI needs to be tested with multicenter external data. This study aimed to investigate the diagnostic efficacy of an AI model in detecting and localizing visible csPCa on mpMRI a multicenter external data set. Methods: The data of 2,105 patients suspected of having prostate cancer from four hospitals were retrospectively collected to develop an AI model to detect and localize suspicious csPCa. The lesions were annotated based on pathology records by two radiologists. Diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values were used as the input for the three-dimensional U-Net framework. Subsequently, the model was validated using an external data set comprising the data of 557 patients from three hospitals. Sensitivity, specificity, and accuracy were employed to evaluate the diagnostic efficacy of the model. Results: At the lesion level, the model had a sensitivity of 0.654. At the overall sextant level, the model had a sensitivity, specificity, and accuracy of 0.846, 0.884, and 0.874, respectively. At the patient level, the model had a sensitivity, specificity, and accuracy of 0.943, 0.776, and 0.849, respectively. The AI-predicted accuracy for the csPCa patients (231/245, 0.943) was significantly higher than that for the non-csPCa patients (242/312, 0.776) (P<0.001). The lesion number and tumor volume were greater in the correctly diagnosed patients than the incorrectly diagnosed patients (both P<0.001). Among the positive patients, those with lower average ADC values had a higher rate of correct diagnosis than those with higher average ADC values (P=0.01). Conclusions: The AI model exhibited acceptable accuracy in detecting and localizing visible csPCa at the patient and sextant levels. However, further improvements need to be made to enhance the sensitivity of the model at the lesion level.

Artificial intelligence as diagnostic aiding tool in cases of Prostate Imaging Reporting and Data System category 3: the results of retrospective multi-center cohort study.

PURPOSE: To study the effect of artificial intelligence (AI) on the diagnostic performance of radiologists in interpreting prostate mpMRI images of the PI-RADS 3 category. METHODS: In this multicenter study, 16 radiologists were invited to interpret prostate mpMRI cases with and without AI. The study included a total of 87 cases initially diagnosed as PI-RADS 3 by radiologists without AI, with 28 cases being clinically significant cancers (csPCa) and 59 cases being non-csPCa. The study compared the diagnostic efficacy between readings without and with AI, the reading time, and confidence levels. RESULTS: AI changed the diagnosis in 65 out of 87 cases. Among the 59 non-csPCa cases, 41 were correctly downgraded to PI-RADS 1-2, and 9 were incorrectly upgraded to PI-RADS 4-5. For the 28 csPCa cases, 20 were correctly upgraded to PI-RADS 4-5, and 5 were incorrectly downgraded to PI-RADS 1-2. Radiologists assisted by AI achieved higher diagnostic specificity and accuracy than those without AI [0.695 vs 0.000 and 0.736 vs 0.322, both P < 0.001]. Sensitivity with AI was not significantly different from that without AI [0.821 vs 1.000, P = 1.000]. AI reduced reading time significantly compared to without AI (mean: 351 seconds, P < 0.001). The diagnostic confidence score with AI was significantly higher than that without AI (Cohen Kappa: -0.016). CONCLUSION: With the help of AI, there was an improvement in the diagnostic accuracy of PI-RADS category 3 cases by radiologists. There is also an increase in diagnostic efficiency and diagnostic confidence.

Clinical study of multifactorial diagnosis in prostate biopsy.

PURPOSE: To study the feasibility of using an artificial intelligence (AI) algorithm for the diagnosis of clinically significant prostate cancer (csPCa) on multiparametric MRI (mpMRI) in combination with conventional clinical information. METHODS: A retrospective study cohort with 505 patients was collected, with complete information on age (≤60, 60-80, and >80 years), PSA (≤4, 4-10, and >10 ng/dL), and pathology results. The patients with ISUP group >2 were classified as csPCa, and the patients with ISUP = 1 or no evidence of prostate cancer were classified as non-csPCa. The diagnosis of mpMRI was made by experienced radiologists following the prostate imaging reporting and data system (PIRADS ≤ 2, PIRADS = 3, and PIRADS > 3). The mpMRI images were processed by a homemade AI algorithm, and the AI results were obtained as positive or negative for csPCa. Two logistic regression models were fitted, with pathological findings as the dependent variable, that is, a conventional model and an AI model. The conventional model used age, PSA, and PIRADS as the independent variables. The AI model took the AI result and the abovementioned clinical information as the independent variables. The predicted probability of the patients from the conventional model and the AI model were used to test the prediction efficacy of the models. The DeLong test was performed to compare differences in the area under the receiver operating characteristic (ROC) area under the curve (AUC) between the conventional model and the AI model. RESULTS: In total, 505 patients were included in the study; 280 were diagnosed with csPCa, and 225 were non-csPCa. The median age was 72.0 (67.0, 76.0) years, with a median PSA value of 13.0 (7.46, 27.5) ng/dL. Statically significant differences were found in age, PSA, PIRADS score and AI results between the csPCa and non-csPCa groups (all p < 0.001). In the multivariable regression models, all the variables were independently associated with csPCa. The conventional model (R2 = 0.361) and the AI model (R2 = 0.474) were compared with analysis of variance (ANOVA) and showed statistically significant differences (χ2 = 63.695, p < 0.001). The AUC of the ROC curve for the conventional model was 0.782 (95% confidence interval [CI]: 0.742-0.823), which was less than the AUC of the AI model with statistical significance (0.849 [95% CI: 0.815-0.883], p < 0.001). CONCLUSION: In combination with routine clinical information, such as age, PSA, and PIRADS category, adding information from the AI algorithm based on mpMRI could improve the diagnosis of csPCa.

AI-predicted mpMRI image features for the prediction of clinically significant prostate cancer.

PURPOSE: To evaluate the feasibility of using mpMRI image features predicted by AI algorithms in the prediction of clinically significant prostate cancer (csPCa). MATERIALS AND METHODS: This study analyzed patients who underwent prostate mpMRI and radical prostatectomy (RP) at the Affiliated Hospital of Jiaxing University between November 2017 and December 2022. The clinical data collected included age, serum prostate-specific antigen (PSA), and biopsy pathology. The reference standard was the prostatectomy pathology, and a Gleason Score (GS) of 3 + 3 = 6 was considered non-clinically significant prostate cancer (non-csPCa), while a GS ≥ 3 + 4 was considered csPCa. A pre-trained AI algorithm was used to extract the lesion on mpMRI, and the image features of the lesion and the prostate gland were analyzed. Two logistic regression models were developed to predict csPCa: an MR model and a combined model. The MR model used age, PSA, PSA density (PSAD), and the AI-predicted MR image features as predictor variables. The combined model used biopsy pathology and the aforementioned variables as predictor variables. The model's effectiveness was evaluated by comparing it to biopsy pathology using the area under the curve (AUC) of receiver operation characteristic (ROC) analysis. RESULTS: A total of 315 eligible patients were enrolled with an average age of 70.8 ± 5.9. Based on RP pathology, 18 had non-csPCa, and 297 had csPCa. PSA, PSAD, biopsy pathology, and ADC value of the prostate outside the lesion (ADCprostate) varied significantly across different ISUP grade groups of RP pathology (P < 0.001). Other clinical variables and image features did not vary significantly across different ISUP grade groups (P > 0.05). The MR model included PSAD, the ratio of ADC value between the lesion and the prostate outside the lesion (ADClesion/prostate), the signal intensity ratio of DWI between the lesion and the prostate outside the lesion (DWIlesion/prostate), and the ratio of DWIlesion/prostate to ADClesion/prostate. The combined model included biopsy pathology, ADClesion/prostate, mean signal intensity of the lesion on DWI (DWIlesion), DWI signal intensity of the prostate outside the lesion (DWIprostate), and signal intensity ratio of DWI between the lesion and the prostate outside the lesion (DWIlesion/prostate). The AUC of the MR model (0.830, 95% CI 0.743, 0.916) was not significantly different from that of biopsy pathology (0.820, 95% CI 0.728, 0.912, P = 0.884). The AUC of the combined model (0.915, 95% CI 0.849, 0.980) was higher than that of the biopsy pathology (P = 0.042) and MR model (P = 0.031). CONCLUSION: The aggressiveness of prostate cancer can be effectively predicted using AI-extracted image features from mpMRI images, similar to biopsy pathology. The prediction accuracy was improved by combining the AI-extracted mpMRI image features with biopsy pathology, surpassing the performance of biopsy pathology alone.

Groundwater quality characteristics and health risk assessment in the valley plain area of the western Qinghai-Tibet plateau.

Accurate evaluation of groundwater chemistry, quality, and human health risk could provide detailed and robust evidence of groundwater utilization. Gaer County is an important residential area in western Tibet. A total of 52 samples were collected from the Shiquan River Basin in Gaer County in 2021. Principal component analysis, ratiometric analysis of major ions, and geochemical modeling were conducted to clarify the characteristics of hydrogeochemical compositions and the controlling factors. The groundwater chemistry type is dominated by HCO3-Ca, and its ion concentration from high to low is Ca2+ > Na+ > Mg2+ > K+ and HCO3- > SO42- > Cl- > NO3- > F-. The groundwater compositions were determined by calcite and dolomite dissolution with cation exchange reaction. The human activity causes nitrate contamination, while arsenic contamination is attributed to surface water recharge. According to the Water Quality Index, 99% of the samples meet the requirements of drinking water. Groundwater quality is affected by the arsenic, fluoride, and nitrate concentrations. According to the human health risk assessment model, the cumulative noncarcinogenic risk (HITotal) values for children and the CR values of arsenic (CRArsenic) for adults are higher than 1 and 1E-6, respectively, which are unacceptable risk values. Therefore, appropriate remedial measures are recommended to reduce nitrate and arsenic concentrations in groundwater sources for protecting against further health risks. This study can provide theoretical support and effective groundwater management experience for ensuring groundwater safety in Gaer County and other similar regions around the world.

A multicenter study of artificial intelligence-aided software for detecting visible clinically significant prostate cancer on mpMRI.

BACKGROUND: AI-based software may improve the performance of radiologists when detecting clinically significant prostate cancer (csPCa). This study aims to compare the performance of radiologists in detecting MRI-visible csPCa on MRI with and without AI-based software. MATERIALS AND METHODS: In total, 480 multiparametric MRI (mpMRI) images were retrospectively collected from eleven different MR devices, with 349 csPCa lesions in 180 (37.5%) cases. The csPCa areas were annotated based on pathology. Sixteen radiologists from four hospitals participated in reading. Each radiologist was randomly assigned to 30 cases and diagnosed twice. Half cases were interpreted without AI, and the other half were interpreted with AI. After four weeks, the cases were read again in switched mode. The mean diagnostic performance was compared using sensitivity and specificity on lesion level and patient level. The median reading time and diagnostic confidence were assessed. RESULTS: On lesion level, AI-aided improved the sensitivity from 40.1% to 59.0% (18.9% increased; 95% confidence interval (CI) [11.5, 26.1]; p < .001). On patient level, AI-aided improved the specificity from 57.7 to 71.7% (14.0% increase, 95% CI [6.4, 21.4]; p < .001) while preserving the sensitivity (88.3% vs. 93.9%, p = 0.06). AI-aided reduced the median reading time of one case by 56.3% from 423 to 185 s (238-s decrease, 95% CI [219, 260]; p < .001), and the median diagnostic confidence score was increased by 10.3% from 3.9 to 4.3 (0.4-score increase, 95% CI [0.3, 0.5]; p < .001). CONCLUSIONS: AI software improves the performance of radiologists by reducing false positive detection of prostate cancer patients and also improving reading times and diagnostic confidence. CLINICAL RELEVANCE STATEMENT: This study involves the process of data collection, randomization and crossover reading procedure.

Deep-Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi-Parametric MRI.

BACKGROUND: Deep learning for diagnosing clinically significant prostate cancer (csPCa) is feasible but needs further evaluation in patients with prostate-specific antigen (PSA) levels of 4-10 ng/mL. PURPOSE: To explore diffusion-weighted imaging (DWI), alone and in combination with T2-weighted imaging (T2WI), for deep-learning-based models to detect and localize visible csPCa. STUDY TYPE: Retrospective. POPULATION: One thousand six hundred twenty-eight patients with systematic and cognitive-targeted biopsy-confirmation (1007 csPCa, 621 non-csPCa) were divided into model development (N = 1428) and hold-out test (N = 200) datasets. FIELD STRENGTH/SEQUENCE: DWI with diffusion-weighted single-shot gradient echo planar imaging sequence and T2WI with T2-weighted fast spin echo sequence at 3.0-T and 1.5-T. ASSESSMENT: The ground truth of csPCa was annotated by two radiologists in consensus. A diffusion model, DWI and apparent diffusion coefficient (ADC) as input, and a biparametric model (DWI, ADC, and T2WI as input) were trained based on U-Net. Three radiologists provided the PI-RADS (version 2.1) assessment. The performances were determined at the lesion, location, and the patient level. STATISTICAL TESTS: The performance was evaluated using the areas under the ROC curves (AUCs), sensitivity, specificity, and accuracy. A P value <0.05 was considered statistically significant. RESULTS: The lesion-level sensitivities of the diffusion model, the biparametric model, and the PI-RADS assessment were 89.0%, 85.3%, and 90.8% (P = 0.289-0.754). At the patient level, the diffusion model had significantly higher sensitivity than the biparametric model (96.0% vs. 90.0%), while there was no significant difference in specificity (77.0%. vs. 85.0%, P = 0.096). For location analysis, there were no significant differences in AUCs between the models (sextant-level, 0.895 vs. 0.893, P = 0.777; zone-level, 0.931 vs. 0.917, P = 0.282), and both models had significantly higher AUCs than the PI-RADS assessment (sextant-level, 0.734; zone-level, 0.863). DATA CONCLUSION: The diffusion model achieved the best performance in detecting and localizing csPCa in patients with PSA levels of 4-10 ng/mL. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.