Detection of intermediate- and high-risk prostate cancer with biparametric magnetic resonance imaging: a systematic review and meta-analysis

Wang, Yajing; Wang, Wei; Yi, Nuo; Jiang, Liang; Xia, Yin; Zhou, Wei; Wang, Liwei

doi:10.21037/qims-22-1024

Cited by 4 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, numerous studies have presented compelling evidence in this domain ( 28 ). The commercially available software provided at https://fuse-ai.de/prostatecarcinoma-ai has a comparable level of reliability to that of radiologists in detecting carcinomas, with a patient-level sensitivity of 0.86, which, in contrast, is inferior to our model’s sensitivity value of 0.94.…”

Section: Discussionmentioning

confidence: 99%

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

Sun,

Wang,

et al. 2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Sun,

Wang,

et al. 2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

show abstract

“…Several studies have confirmed that the diagnostic accuracy of using PI-RADS outperforms the traditional serum PSA test or digital rectal examination. Although using mpMRI imaging can mitigate the overdiagnosis of PCa, false-positive and false-negative results are often encountered in clinical practice ( 11 - 13 ). The use of mpMRI as a triage test prior to prostate biopsy remains controversial.…”

Section: Introductionmentioning

confidence: 99%

Creating a novel multiparametric magnetic resonance imaging-based biopsy strategy for reducing unnecessary prostate biopsies: a retrospective cohort study

Wang,

Shen,

Yuan

et al. 2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background The overdiagnosis of prostate cancer (PCa) caused by unnecessary prostate biopsy has become a worldwide problem that urgently requires a solution. We aimed to reduce the unnecessary prostate biopsies and increase the detection rate of clinically significant PCa (csPCa) by creating a novel multiparametric magnetic resonance imaging (mpMRI)-based strategy. Methods A total of 1,194 eligible patients who underwent transperineal prostate biopsies from January 2018 to December 2022 were included in this retrospective study. Of these patients, 1,080 who received prostate biopsies from January 2018 to July 2022 were regarded as cohort 1 for primary analysis, and 114 patients who received prostate biopsies from August 2022 to December 2022 were collected in cohort 2 for validation. All the mpMRI images were quantitatively evaluated by the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v. 2.1). The diagnostic performances were assessed through the receiver operating characteristic (ROC) curve and area under the curve (AUC) and were compared with the DeLong test. Cancer diagnosis–free survival analysis was performed using the Kaplan-Meier method and log-rank test. The primary endpoint of this study was clinically significant PCa with an International Society of Urological Pathology (ISUP) grade ≥2. Results In cohort 1, the results of ROC curves demonstrated that the PI-RADS score had a higher diagnostic accuracy (AUC =0.898 for any-grade PCa; AUC =0.917 for csPCa) than did the other clinical variables (P<0.001). Under the novel mpMRI-based biopsy strategy, all patients with PI-RADS 1 can safely avoid prostate biopsy. For patients with PI-RADS 2, prostate biopsy should be considered for patients with prostate-specific antigen density (PSAD) ≥0.3 ng/mL 2 and prostate volume <65 mL. As for patients with PI-RADS 3, structured surveillance programs can be a viable option if PSAD <0.3 ng/mL 2 and prostate volume ≥65 mL. Finally, patients with a PI-RADS score of 4 and 5 should undergo prostate biopsy due to the high probability of clinically significant PCa. In the validation analysis of cohort 2, 48 patients were placed into a biopsy-spared group with no csPCa cases, while 66 patients were placed in a biopsy-needed group, with an csPCa detection rate of 50.0%. Overall, the novel strategy demonstrated a sensitivity, specificity, positive predictive value, and negative predictive value of 98.9%, 57.5%, 50.5%, and 99.2%, respectively, for diagnosing csPCa. Conclusions An mpMRI-based biopsy strategy can effectively avoid about 40% of prostate biopsies and maintain a high detection rate for clinically significant PCa. It can further provide valuable guidance for patients and physicians in considering the necessity of prostate biopsy.

show abstract

Enhancing prostate cancer segmentation on multiparametric magnetic resonance imaging with background information and gland masks

Wang,

Sun,

Wei

et al. 2024

Medical Physics

View full text Add to dashboard Cite

BackgroundThe landscape of prostate cancer (PCa) segmentation within multiparametric magnetic resonance imaging (MP‐MRI) was fragmented, with a noticeable lack of consensus on incorporating background details, culminating in inconsistent segmentation outputs. Given the complex and heterogeneous nature of PCa, conventional imaging segmentation algorithms frequently fell short, prompting the need for specialized research and refinement.PurposeThis study sought to dissect and compare various segmentation methods, emphasizing the role of background information and gland masks in achieving superior PCa segmentation. The goal was to systematically refine segmentation networks to ascertain the most efficacious approach.MethodsA cohort of 232 patients (ages 61–73 years old, prostate‐specific antigen: 3.4–45.6 ng/mL), who had undergone MP‐MRI followed by prostate biopsies, was analyzed. An advanced segmentation model, namely Attention‐Unet, which combines U‐Net with attention gates, was employed for training and validation. The model was further enhanced through a multiscale module and a composite loss function, culminating in the development of Matt‐Unet. Performance metrics included Dice Similarity Coefficient (DSC) and accuracy (ACC).ResultsThe Matt‐Unet model, which integrated background information and gland masks, outperformed the baseline U‐Net model using raw images, yielding significant gains (DSC: 0.7215 vs. 0.6592; ACC: 0.8899 vs. 0.8601, p < 0.001).ConclusionA targeted and practical PCa segmentation method was designed, which could significantly improve PCa segmentation on MP‐MRI by combining background information and gland masks. The Matt‐Unet model showcased promising capabilities for effectively delineating PCa, enhancing the precision of MP‐MRI analysis.

show abstract

Detection of intermediate- and high-risk prostate cancer with biparametric magnetic resonance imaging: a systematic review and meta-analysis

Cited by 4 publications

References 39 publications

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

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

Creating a novel multiparametric magnetic resonance imaging-based biopsy strategy for reducing unnecessary prostate biopsies: a retrospective cohort study

Enhancing prostate cancer segmentation on multiparametric magnetic resonance imaging with background information and gland masks

Contact Info

Product

Resources

About