Bladder cancer (BC) is the 10th most common cancer globally and has a high mortality rate if not detected early and treated promptly. Non-muscle-invasive BC (NMIBC) is a subclassification of BC associated with high rates of recurrence and progression. Current tools for predicting recurrence and progression on NMIBC use scoring systems based on clinical and histopathological markers. These exclude other potentially useful biomarkers which could provide a more accurate personalized risk assessment. Future trends are likely to use artificial intelligence (AI) to enhance the prediction of recurrence in patients with NMIBC and decrease the use of standard clinical protocols such as cystoscopy and cytology. Here, we provide a comprehensive survey of the most recent studies from the last decade (N = 70 studies), focused on the prediction of patient outcomes in NMIBC, particularly recurrence, using biomarkers such as radiomics, histopathology, clinical, and genomics. The value of individual and combined biomarkers is discussed in detail with the goal of identifying future trends that will lead to the personalized management of NMIBC.

The primary objective of this paper is to develop a machine learning-based approach capable of predicting the treatment response of neoadjuvant chemotherapy (NAC) to enhance breast cancer treatment management. The proposed system aims to predict NAC outcomes across three categories: pathological complete response (CR), partial response (PR), and stable disease (SD), by analyzing multimodal magnetic resonance images with clinical and molecular subtype markers. To ensure the comprehensiveness of our system design, texture radiomics were extracted from T1, T2, and STIR MRI modalities, along with functional radiomics from diffusion-weighted MRI at various b-values. The main rationale behind employing multiple b-values in collecting DW-MRI is to effectively capture the complexities of blood diffusion within the tumor microstructure. The proposed system comprises several key steps: i) extracting texture and functional radiomics from T1, T2, STIR MRI, and DW-MRI data; ii) identifying the most significant radiomics correlated with NAC treatment using a genetic algorithm; iii) initially predicting the PR from alternative treatment responses utilizing the extracted textures and functional radiomics; and iv) subsequently integrating clinical and molecular subtype markers with imaging radiomics to differentiate between CR and SD. Our proposed system is trained and validated through the utilization of a leave-one-subject-out (LOSO) cross-validation approach on various MRI scans from 109 subjects, of whom 27 had complete responses, 54 had partial responses, and 28 had no responses. The performance of the proposed system was assessed through the utilization of Cohen’s Kappa and accuracy metrics, achieving 81.31% and 88.07%, respectively. Our various experiments showed that integrating clinical and molecular subtype markers with radiomics highlights the proposed system’s efficiency in evaluating the tumor’s response to NAC efficiently, outperforming predictions based solely on individual radiomics.

The grading of non-muscle invasive bladder cancer (NMIBC) continues to face challenges due to subjective interpretations, which affect the assessment of its severity. To address this challenge, we are developing an innovative artificial intelligence (AI) system aimed at objectively grading NMIBC. This system uses a novel convolutional neural network (CNN) architecture called the multi-scale pyramidal pretrained CNN to analyze both local and global pathology markers extracted from digital pathology images. The proposed CNN structure takes as input three levels of patches, ranging from small patches (e.g., \(128 \times 128\)) to the largest size patches (\(512 \times 512\)). These levels are then fused by random forest (RF) to estimate the severity grade of NMIBC. The optimal patch sizes and other model hyperparameters are determined using a grid search algorithm. For each patch size, the proposed system has been trained on 32K patches (comprising 16K low-grade and 16K high-grade samples) and subsequently tested on 8K patches (consisting of 4K low-grade and 4K high-grade samples), all annotated by two pathologists. Incorporating light and efficient processing, defining new benchmarks in the application of AI to histopathology, the ShuffleNet-based AI system achieved notable metrics on the testing data, including 94.25% ± 0.70% accuracy, 94.47% ± 0.93% sensitivity, 94.03% ± 0.95% specificity, and a 94.29% ± 0.70% F1-score. These results highlight its superior performance over traditional models like ResNet-18. The proposed system’s robustness in accurately grading pathology demonstrates its potential as an advanced AI tool for diagnosing human diseases in the domain of digital pathology.

Breast cancer is a prevalent and diverse type of cancer that exhibits unique clinicopathologic characteristics, making the correct identification of its subtype critical to providing targeted treatment and increasing survival rates. This identification process involves testing for the presence of four key molecular biomarkers, namely estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and antigen Ki67. For accurate diagnosis ,the expertise of a pathologist and immunohistochemistry is required. To overcome this diagnostic challenge, we present a novel approach based on a deep learning pipeline for automated classification. Our approach can detect tumor and non-tumoral regions of the HER2 biomarker. Our deep learning framework comprises a Dense Convolutional Network (DenseNet), which process whole slide images (WSIs) of breast tissues, dividing them into patches for input into the network. Moreover, our approach provides both patchwise and pixelwise classification and analyzes ten WSIs of breast cancer histology. Our proposed approach generates an image map that classifies slide images on the pixel-level, detecting the status of hormone HER2 receptor as either positive or negative. The obtained results show that our deep learning-based approach has the potential to enhance the pathologist’s capabilities in diagnosing histopathological images with automated classification.