The added value of AI-based computer-aided diagnosis in classification of cancer at prostate MRI

Liu, Guiqin; Zhao, Rui; Zhou, Hang; Chen, Jie; Zhou, Xiang; Xu, Jianrong; Zhou, Yan; Xue, Wei; Pan, Shihang

doi:10.1007/s00330-023-09433-2

Cited by 9 publications

(3 citation statements)

References 28 publications

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“…The study of Silva et al [33], despite enhancing the model's generalizability through federated learning for inter-hospital data sharing, remained limited to a single imaging modality and did not integrate non-imaging data. Liu et al [34] discussed the limitations of single-modal machine learning methods in predicting LNM, emphasizing the importance of multi-modal and multi-center data, and suggested future research directions.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, some researchers explored the value of transfer learning in automated CC tumor segmentation [37], successfully applying it to the diffusion-weighted magnetic resonance imaging of uterine malignant tumors. Liu et al [34] presented a fresh perspective on machine learning in MRI-image diagnosis of prostate cancer. These innovative approaches provide novel viewpoints on how to apply existing knowledge to new areas.…”

Section: Discussionmentioning

confidence: 99%

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Enhancing the Accuracy of Lymph-Node-Metastasis Prediction in Gynecologic Malignancies Using Multimodal Federated Learning: Integrating CT, MRI, and PET/CT

Hu,

Ma,

Ding

et al. 2023

Cancers

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Gynecological malignancies, particularly lymph node metastasis, have presented a diagnostic challenge, even with traditional imaging techniques such as CT, MRI, and PET/CT. This study was conceived to explore and, subsequently, to bridge this diagnostic gap through a more holistic and innovative approach. By developing a comprehensive framework that integrates both non-image data and detailed MRI image analyses, this study harnessed the capabilities of a multimodal federated-learning model. Employing a composite neural network within a federated-learning environment, this study adeptly merged diverse data sources to enhance prediction accuracy. This was further complemented by a sophisticated deep convolutional neural network with an enhanced U-NET architecture for meticulous MRI image processing. Traditional imaging yielded sensitivities ranging from 32.63% to 57.69%. In contrast, the federated-learning model, without incorporating image data, achieved an impressive sensitivity of approximately 0.9231, which soared to 0.9412 with the integration of MRI data. Such advancements underscore the significant potential of this approach, suggesting that federated learning, especially when combined with MRI assessment data, can revolutionize lymph-node-metastasis detection in gynecological malignancies. This paves the way for more precise patient care, potentially transforming the current diagnostic paradigm and resulting in improved patient outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Enhancing the Accuracy of Lymph-Node-Metastasis Prediction in Gynecologic Malignancies Using Multimodal Federated Learning: Integrating CT, MRI, and PET/CT

Hu,

Ma,

Ding

et al. 2023

Cancers

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“…Despite this improvement, Boellaard et al [ 8 ] emphasized the need for artificial intelligence (AI) to further improve interobserver agreement through automated measurements of pelvic parameters. AI models can be used to segment relevant structures and improve the consistency and efficiency of radiologists on prostate MRI [ 9 ]. Small deviations in pelvic measurements can have a significant impact on the predictive probability of continence recovery when incorporating them into urinary incontinence prediction models and can influence the treatment choices of PCa patients [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Automated pelvic MRI measurements associated with urinary incontinence for prostate cancer patients undergoing radical prostatectomy

van den Berg,

Spaans,

Wessels

et al. 2024

Eur Radiol Exp

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Background Pelvic morphological parameters on magnetic resonance imaging (MRI), such as the membranous urethral length (MUL), can predict urinary incontinence after radical prostatectomy but are prone to interobserver disagreement. Our objective was to improve interobserver agreement among radiologists in measuring pelvic parameters using deep learning (DL)-based segmentation of pelvic structures on MRI scans. Methods Preoperative MRI was collected from 167 prostate cancer patients undergoing radical prostatectomy within our regional multicentric cohort. Two DL networks (nnU-Net) were trained on coronal and sagittal scans and evaluated on a test cohort using an 80/20% train-test split. Pelvic parameters were manually measured by three abdominal radiologists on raw MRI images and with the use of DL-generated segmentations. Automated measurements were also performed for the pelvic parameters. Interobserver agreement was evaluated using the intraclass correlation coefficient (ICC) and the Bland–Altman plot. Results The DL models achieved median Dice similarity coefficient (DSC) values of 0.85–0.97 for coronal structures and 0.87–0.98 for sagittal structures. When radiologists used DL-generated segmentations of pelvic structures, the interobserver agreement for sagittal MUL improved from 0.64 (95% confidence interval 0.28–0.83) to 0.91 (95% CI 0.84–0.95). Furthermore, there was an increase in ICC values for the obturator internus muscle from 0.74 (95% CI 0.42–0.87) to 0.86 (95% CI 0.75–0.92) and for the levator ani muscle from 0.40 (95% CI 0.05–0.66) to 0.61 (95% CI 0.31–0.78). Conclusions DL-based automated segmentation of pelvic structures improved interobserver agreement in measuring pelvic parameters on preoperative MRI scans. Relevance statement The implementation of deep learning segmentations allows for more consistent measurements of pelvic parameters by radiologists. Standardized measurements are crucial for incorporating these parameters into urinary continence prediction models. Key points • DL-generated segmentations improve interobserver agreement for pelvic measurements among radiologists. • Membranous urethral length measurement improved from substantial to almost perfect agreement. • Artificial intelligence enhances objective pelvic parameter assessment for continence prediction models. Graphical Abstract

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

Wang,

Sun,

Wei

et al. 2024

Medical Physics

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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.

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The added value of AI-based computer-aided diagnosis in classification of cancer at prostate MRI

Cited by 9 publications

References 28 publications

Enhancing the Accuracy of Lymph-Node-Metastasis Prediction in Gynecologic Malignancies Using Multimodal Federated Learning: Integrating CT, MRI, and PET/CT

Enhancing the Accuracy of Lymph-Node-Metastasis Prediction in Gynecologic Malignancies Using Multimodal Federated Learning: Integrating CT, MRI, and PET/CT

Automated pelvic MRI measurements associated with urinary incontinence for prostate cancer patients undergoing radical prostatectomy

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

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