Real‐world assessment of a deep learning neural network algorithm for prostate cancer detection in MRI using true de novo data

Deep Learning Methods for Breast Cancer Detection, Classification, and Segmentation Using MRI Scans: A Systematic Review

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, underscoring the importance of early and accurate diagnosis to improve patient outcomes. Magnetic resonance imaging (MRI) is a highly sensitive imaging modality for detecting breast malignancies, particularly in patients with dense breast tissue or those at high risk, where conventional imaging techniques may have limited sensitivity. Recent advances in deep learning (DL) have demonstrated considerable potential for improving the automated analysis of breast MRI, including tumour classification, prediction, and segmentation. This systematic review synthesises peer-reviewed studies published between 2014 and 2025 that exclusively applied DL techniques to breast MRI for cancer classification, prediction, or segmentation. The included studies were critically evaluated with respect to model architectures, dataset characteristics, image preprocessing methods, validation strategies, and reported performance metrics. The reviewed literature demonstrates that DL models consistently achieve high diagnostic performance and have the potential to enhance radiological workflows by supporting automated lesion detection and clinical decision-making. However, several challenges continue to limit their translation into routine clinical practice, including limited access to large, diverse, and well-annotated datasets, inadequate external validation, variability in MRI acquisition protocols, and concerns regarding model interpretability and generalisability. Future research should prioritise the development of robust, explainable, and clinically validated DL models trained on multicentre datasets using standardised evaluation frameworks. Addressing these challenges will be essential to improve the reliability, reproducibility, and clinical applicability of AI-assisted breast cancer diagnosis using MRI.

Deep learning-based detection of pediatric brain tumor presence on MRI

The CNN demonstrates strong potential as a diagnostic aid for detection brain tumors using pediatric MRI images, and its integration into clinical environments could contribute to more objective interpretation, optimize diagnostic workflows, and provide imaging follow-up before and after treatment.

Overcoming clinical data constraints and class imbalance: enhancing brain tumor diagnosis with deep learning on real-world MRI datasets

This study presents innovative approaches for diagnosing and classifying brain tumors from MRI images using advanced deep learning models to address clinical data constraints — including single-center acquisition, slice-level (not pixel-level) labeling, and real-world imaging variability — alongside extreme class imbalance. By combining convolutional neural networks (CNNs) with recurrent architectures and applying optimization algorithms like Adam and RMSprop, we improve detection accuracy and enhance interpretability. A dataset of 540 MRI images was collected from Hospital, covering various tumor types and patient backgrounds. Preprocessing steps such as normalization, segmentation, and texture-based feature extraction were applied to enhance data quality and model performance. A key innovation of this work is the use of a deep pre-trained model (VGG16), which demonstrates strong generalization potential for future clinical applications. Additionally, our use of real-world hospital data—more challenging than standard Kaggle datasets—makes our results more applicable in practice. Experimental results show that CNN-based models, especially VGG16 and ResNet v2, significantly outperform traditional methods. The VGG16 model achieved a classification accuracy of 97.1%, compared to 85.75% for Random Forest and 83.00% for Support Vector Machine. Crucially, these improvements reveal a critical trade-off: while macro-accuracy reaches 97.1%, Metastatic tumor recall remains at 75% — underscoring that clinical AI must prioritize equitable error distribution over aggregate metrics. Despite these advances, challenges remain, including imbalanced data and preprocessing limitations. Future research should focus on better data balancing and hybrid optimization strategies. This study contributes a robust framework for brain tumor detection, offering practical value for medical imaging and improved patient outcomes.

Preoperative prediction of positive surgical margins in prostate cancer using multimodal deep learning model: a multicenter study.

STL‐DeepBDC: A Robust Few‐Shot Learning Framework for Multiclass Ovarian Tumor Classification in Ultrasound Outperforms Conventional Transfer Learning

An exploratory deep learning framework for multiclass ovarian tumor classification under small‐sample conditions is developed and evaluated.

Toward Autonomous Prostate Cancer Clinical Significance Determination from Spectral/Statistics Features in Bi-Parametric MRI

Simple Summary Deciding between active surveillance and treatment for prostate cancer patients often requires accurate assessment of prostate tumors. Independent fast quantitative analysis may provide a check and support for radiologists who conventionally visually inspect MRI and may follow protocols such as PI-RADS. Artificial intelligence is increasingly being applied to help clinical assessment of prostate tumors detected with multi-parametric MRI, just as in other fields. However, implementation of artificial intelligence and deep learning is computationally intensive, requires fast, high-end processing boards and, therefore, uses considerable energy and water resources for cooling. A less intensive, less wasteful, cheaper, and less environmentally destructive assessment approach is needed. The simpler spectral/statistics approach that mimics color vision was previously successfully applied in retrospective pilot studies of bi-parametric MRI of prostate cancer. The novel approach needs far fewer resources, is less computationally intensive, and is simpler than artificial intelligence to evaluate prostate tumors. However, these earlier spectral/statistics pilot studies required the intervention of an analyst and too much time for implementation in future large patient studies that are needed to validate the novel approach. This study developed, applied, and tested new automation tools to expedite the spectral/statistics approach. Automating the novel approach resulted in sufficiently high AUCs and a reduction in processing time, warranting future applications in large patient cohorts.