Dataset Information

Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images.

ABSTRACT: Early detection of prostate cancer (PCa) and benign prostatic hyperplasia (BPH) is crucial for maintaining the health and well-being of aging male populations. This study aims to evaluate the performance of transfer learning with convolutional neural networks (CNNs) for efficient classification of PCa and BPH in transrectal ultrasound (TRUS) images. A retrospective experimental design was employed in this study, with 1380 TRUS images for PCa and 1530 for BPH. Seven state-of-the-art deep learning (DL) methods were employed as classifiers with transfer learning applied to popular CNN architectures. Performance indices, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), Kappa value, and Hindex (Youden's index), were used to assess the feasibility and efficacy of the CNN methods. The CNN methods with transfer learning demonstrated a high classification performance for TRUS images, with all accuracy, specificity, sensitivity, PPV, NPV, Kappa, and Hindex values surpassing 0.9400. The optimal accuracy, sensitivity, and specificity reached 0.9987, 0.9980, and 0.9980, respectively, as evaluated using twofold cross-validation. The investigated CNN methods with transfer learning showcased their efficiency and ability for the classification of PCa and BPH in TRUS images. Notably, the EfficientNetV2 with transfer learning displayed a high degree of effectiveness in distinguishing between PCa and BPH, making it a promising tool for future diagnostic applications.

SUBMITTER: Huang TL

PROVIDER: S-EPMC10710441 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images.

Huang Te-Li TL Lu Nan-Han NH Huang Yung-Hui YH Twan Wen-Hung WH Yeh Li-Ren LR Liu Kuo-Ying KY Chen Tai-Been TB

Scientific reports 20231209 1

Early detection of prostate cancer (PCa) and benign prostatic hyperplasia (BPH) is crucial for maintaining the health and well-being of aging male populations. This study aims to evaluate the performance of transfer learning with convolutional neural networks (CNNs) for efficient classification of PCa and BPH in transrectal ultrasound (TRUS) images. A retrospective experimental design was employed in this study, with 1380 TRUS images for PCa and 1530 for BPH. Seven state-of-the-art deep learning ...[more]

PMID: 38071254

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Project description:Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g., speckle and shadowing) in ultrasound images limit the performance of automated prostate segmentation techniques and generalizing these methods to new image domains is inherently difficult. In this study, we address these challenges by introducing a novel 2.5D deep neural network for prostate segmentation on ultrasound images. Our approach addresses the limitations of transfer learning and finetuning methods (i.e., drop in performance on the original training data when the model weights are updated) by combining a supervised domain adaptation technique and a knowledge distillation loss. The knowledge distillation loss allows the preservation of previously learned knowledge and reduces the performance drop after model finetuning on new datasets. Furthermore, our approach relies on an attention module that considers model feature positioning information to improve the segmentation accuracy. We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions. We analyzed the performance of our method on three large datasets encompassing 2067 subjects from three different institutions. Our method achieved an average Dice Similarity Coefficient (Dice) of 94.0±0.03 and Hausdorff Distance (HD95) of 2.28 mm in an independent set of subjects from the first institution. Moreover, our model generalized well in the studies from the other two institutions (Dice: 91.0±0.03; HD95: 3.7 mm and Dice: 82.0±0.03; HD95: 7.1 mm). We introduced an approach that successfully segmented the prostate on ultrasound images in a multi-center study, suggesting its clinical potential to facilitate the accurate fusion of ultrasound and MRI images to drive biopsy and image-guided treatments.

Project description:PurposeTo construct machine learning models based on radiomics features combing conventional transrectal ultrasound (B-mode) and contrast-enhanced ultrasound (CEUS) to improve prostate cancer (PCa) detection in peripheral zone (PZ).MethodsA prospective study of 166 men (72 benign, 94 malignant lesions) with targeted biopsy-confirmed pathology who underwent B-mode and CEUS examinations was performed. Risk factors, including age, serum total prostate-specific antigen (tPSA), free PSA (fPSA), f/t PSA, prostate volume and prostate-specific antigen density (PSAD), were collected. Time-intensity curves were obtained using SonoLiver software for all lesions in regions of interest. Four parameters were collected as risk factors: the maximum intensity (IMAX), rise time (RT), time to peak (TTP), and mean transit time (MTT). Radiomics features were extracted from the target lesions from B-mode and CEUS imaging. Multivariable logistic regression analysis was used to construct the model.ResultsA total of 3306 features were extracted from seven categories. Finally, 32 features were screened out from radiomics models. Five models were developed to predict PCa: the B-mode radiomics model (B model), CEUS radiomics model (CEUS model), B-CEUS combined radiomics model (B-CEUS model), risk factors model, and risk factors-radiomics combined model (combined model). Age, PSAD, tPSA, and RT were significant independent predictors in discriminating benign and malignant PZ lesions (P < 0.05). The risk factors model combing these four predictors showed better discrimination in the validation cohort (area under the curve [AUC], 0.84) than the radiomics images (AUC, 0.79 on B model; AUC, 0.78 on CEUS model; AUC, 0.83 on B-CEUS model), and the combined model (AUC: 0.89) achieved the greatest predictive efficacy.ConclusionThe prediction model including B-mode and CEUS radiomics signatures and risk factors represents a promising diagnostic tool for PCa detection in PZ, which may contribute to clinical decision-making.

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Project description:PurposeThe identification of clinically significant disease is crucial for optimal treatment of prostate cancer. Selective detection of prostate cancer with increased microvessel density is possible with contrast enhanced ultrasound. Preliminary studies suggest that pretreatment with a 5α-reductase inhibitor may improve the efficiency of contrast enhanced ultrasound targeted biopsy. This study was designed to quantify prostate cancer detection with contrast enhanced ultrasound with or without short-term pretreatment with dutasteride.Materials and methodsIn this randomized, double-blind, placebo controlled trial of oral dutasteride pretreatment, contrast enhanced ultrasound findings were graded and used to direct targeted biopsy (up to 6 cores per prostate). A blinded 12-core systematic biopsy was subsequently performed on every subject based on standard medial and lateral sampling of each sextant.ResultsOf 311 subjects who underwent randomization, 272 completed participation. Positive biopsies were obtained in 276 of 3,264 (8.5%) systematic cores and 203 of 1,237 (16.4%) targeted cores (OR 2.1, 95% CI 1.7-2.6, p <0.001). ROC analysis for the detection of all prostate cancers demonstrated an increase in diagnostic accuracy from pre-contrast imaging to contrast enhanced ultrasound (A(z) 0.60 vs 0.64, p = 0.005). For the detection of high grade cancer (Gleason score 7 or greater) ROC analysis demonstrated improved accuracy for pre-contrast imaging (A(z) 0.74) and contrast enhanced ultrasound (A(z) 0.80, p = 0.0005). For the detection of high grade cancer with greater than 50% biopsy core involvement, excellent accuracy was demonstrated with pre-contrast and contrast enhanced ultrasound, A(z) 0.83 and 0.90, respectively (p = 0.001). Pretreatment with dutasteride had no significant impact on the detection of prostate cancer (p = 0.97).ConclusionsContrast enhanced ultrasound targeted biopsy provides a significant benefit for the detection of high grade/high volume prostate cancer.

Dataset Information

Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images.

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Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images.

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