Whole slide image (WSI) analysis is gaining prominence within the medical imaging field.

The histopathological images contain a huge amount of information, which can make diagnosis an extremely timeconsuming and tedious task. In this study, we developed a completely automated system to detect regions of interest (ROIs) in whole slide images (WSI) of renal cell carcinoma (RCC), to reduce time analysis and assist pathologists in making more accurate decisions. The proposed approach is based on an efficient texture descriptor named dominant rotated local binary pattern (DRLBP) and color transformation to reveal and exploit the immense texture variability at the microscopic high magnifications level. Thereby, the DRLBPs retain the structural information and utilize the magnitude values in a local neighborhood for more discriminative power. For the classification of the relevant ROIs, feature extraction of WSIs patches was performed on the color channels separately to form the histograms. Next, we used the most frequently occurring patterns as a feature selection step to discard non-informative features. The performances of different classifiers on a set of 1800 kidney cancer patches originating from 12 whole slide images were compared and evaluated. Furthermore, the small size of the image dataset allows to investigate deep learning approach based on transfer learning for image patches classification by using deep features and fine-tuning methods. High recognition accuracy was obtained and the classifiers are efficient, the best precision result was 99.17% achieved with SVM. Moreover, transfer learning models perform well with comparable performance, and the highest precision using ResNet-50 reached 98.50%. The proposed approach results revealed a very efficient image classification and demonstrated efficacy in identifying ROIs. This study presents an automatic system to detect regions of interest relevant to the diagnosis of kidney cancer in whole slide histopathology images.

Its mortality rate is considered high, with respect to its incidence rate, as this tumor is typically asymptomatic at the early stages for many patients [1, 2]. This leads to a late diagnosis of the tumor, where the curability likelihood is lower. RCC can be categorized into multiple histological subtypes, mainly: Clear Cell Renal Cell Carcinoma (ccRCC) forming 75% of RCCs, Papillary Renal Cell Carcinoma (pRCC) accounting for 10%, and Chromophobe Renal Cell Carcinoma (chRCC) accounting for 5%. Some of the other sutypes include Collecting Duct Renal Cell Carcinoma (cdRCC), Tubulocystic Renal Cell Carcinoma (tRCC), and unclassified [1]. Approximately 10% of renal tumors belong to the benign entities neoplasms, being Oncocytoma (ONCO) the most frequent subtype with an incidence of 3-7% among all RCCs [3, 2]. These subtypes show different cytological signature as well as histological features [2], which ends up in significantly different prognosis. The correct categorization of the tumor subtype is indeed of major importance, as prognosis and treatment approaches depend on it and on the disease stage. For instance, the overall 5-year survival rate significantly differs among the different histological subtypes, being 55-60% for ccRCC, 80-90% for pRCC and 90% for chRCC.

Many incurable diseases prevalent across global societies stem from various influences, including lifestyle choices, economic conditions, social factors, and genetics. Research predominantly focuses on these diseases due to their widespread nature, aiming to decrease mortality, enhance treatment options, and improve healthcare standards. Among these, kidney disease stands out as a particularly severe condition affecting men and women worldwide. Nonetheless, there is a pressing need for continued research into innovative, early diagnostic methods to develop more effective treatments for such diseases. Recently, automatic diagnosis of Kidney Cancer has become an important challenge especially when using deep learning (DL) due to the importance of training medical datasets, which in most cases are difficult and expensive to obtain. Furthermore, in most cases, algorithms require data from the same domain and a powerful computer with efficient storage capacity. To overcome this issue, a new type of learning known as transfer learning (TL) has been proposed that can produce impressive results based on other different pre-trained data. This paper presents, to the best of the authors' knowledge, the first comprehensive survey of DL-based TL frameworks for kidney cancer diagnosis. This is a strong contribution to help researchers understand the current challenges and perspectives of this topic. Hence, the main limitations and advantages of each framework are identified and detailed critical analyses are provided. Looking ahead, the article identifies promising directions for future research. Moving on, the discussion is concluded by reflecting on the pivotal role of TL in the development of precision medicine and its effects on clinical practice and research in oncology.

Background: Predictive biomarkers of treatment response are lacking for metastatic clearcell renal cell carcinoma (ccRCC), a tumor type that is treated with angiogenesis inhibitors, immune checkpoint inhibitors, mTOR inhibitors and a HIF2 inhibitor. The Angioscore, an RNA-based quantification of angiogenesis, is arguably the best candidate to predict anti-angiogenic (AA) response. However, the clinical adoption of transcriptomic assays faces several challenges including standardization, time delay, and high cost. Further, ccRCC tumors are highly heterogenous, and sampling multiple areas for sequencing is impractical. Approach: Here we present a novel deep learning (DL) approach to predict the Angioscore from ubiquitous histopathology slides. In order to overcome the lack of interpretability, one of the biggest limitations of typical DL models, our model produces a visual vascular network which is the basis of the model's prediction. To test its reliability, we applied this model to multiple cohorts including a clinical trial dataset. Results: Our model accurately predicts the RNA-based Angioscore on multiple independent cohorts (spearman correlations of 0.77 and 0.73). Further, the predictions help unravel meaningful biology such as association of angiogenesis with grade, stage, and driver mutation status. Finally, we find our model is able to predict response to AA therapy, in both a real-world cohort and the IMmotion150 clinical trial. The predictive power of our model vastly exceeds that of CD31, a marker of vasculature, and nearly rivals the performance (c-index 0.66 vs 0.67) of the ground truth RNA-based Angioscore at a fraction of the cost. Conclusion: By providing a robust yet interpretable prediction of the Angioscore from histopathology slides alone, our approach offers insights into angiogenesis biology and AA treatment response. Introduction: Patients with metastatic clear cell renal cell carcinoma (ccRCC) are treated with anti-angiogenic (AA) therapies (e.g., vascular endothelial growth factor tyrosine kinase inhibitors VEGF-TKIs), immune checkpoint inhibitors (ICI), mammalian target of rapamycin (mTOR) inhibitors and a hypoxia inducible factor (HIF)-2 inhibitor, either in combination or as monotherapy (1).

Multi-phase CT is widely adopted for the diagnosis of kidney cancer due to the complementary information among phases. However, the complete set of multi-phase CT is often not available in practical clinical applications. In recent years, there have been some studies to generate the missing modality image from the available data. Nevertheless, the generated images are not guaranteed to be effective for the diagnosis task. In this paper, we propose a unified framework for kidney cancer diagnosis with incomplete multi-phase CT, which simultaneously recovers missing CT images and classifies cancer subtypes using the completed set of images. The advantage of our framework is that it encourages a synthesis model to explicitly learn to generate missing CT phases that are helpful for classifying cancer subtypes. We further incorporate lesion segmentation network into our framework to exploit lesion-level features for effective cancer classification in the whole CT volumes. The proposed framework is based on fully 3D convolutional neural networks to jointly optimize both synthesis and classification of 3D CT volumes. Extensive experiments on both in-house and external datasets demonstrate the effectiveness of our framework for the diagnosis with incomplete data compared with state-of-the-art baselines. In particular, cancer subtype classification using the completed CT data by our method achieves higher performance than the classification using the given incomplete data.

Objectives To develop and validate a deep learning-based diagnostic model incorporating uncertainty estimation so as to facilitate radiologists in the preoperative differentiation of the pathological subtypes of renal cell carcinoma (RCC) based on CT images. Methods Data from 668 consecutive patients, pathologically proven RCC, were retrospectively collected from Center 1. By using five-fold cross-validation, a deep learning model incorporating uncertainty estimation was developed to classify RCC subtypes into clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC). An external validation set of 78 patients from Center 2 further evaluated the model's performance. Results In the five-fold cross-validation, the model's area under the receiver operating characteristic curve (AUC) for the classification of ccRCC, pRCC, and chRCC was 0.868 (95% CI: 0.826-0.923), 0.846 (95% CI: 0.812-0.886), and 0.839 (95% CI: 0.802-0.88), respectively. In the external validation set, the AUCs were 0.856 (95% CI: 0.838-0.882), 0.787 (95% CI: 0.757-0.818), and 0.793 (95% CI: 0.758-0.831) for ccRCC, pRCC, and chRCC, respectively. Conclusions The developed deep learning model demonstrated robust performance in predicting the pathological subtypes of RCC, while the incorporated uncertainty emphasized the importance of understanding model confidence, which is crucial for assisting clinical decision-making for patients with renal tumors. Clinical relevance statement Our deep learning approach, integrated with uncertainty estimation, offers clinicians a dual advantage: accurate RCC subtype predictions complemented by diagnostic confidence references, promoting informed decision-making for patients with RCC.

Renal cell carcinoma (RCC) is a type of cancer that originates in the kidneys and is the most common type of kidney cancer in adults. It can be classified into several subtypes, including clear cell RCC, papillary RCC, and chromophobe RCC. In this study, an artificial intelligence model was developed and trained for classifying different subtypes of RCC using ResNet-18, a convolutional neural network that has been widely used for image classification tasks. The model was trained on a dataset of RCC histopathology images, which consisted of digital images of RCC surgical resection slides that were annotated with the corresponding subtype labels. The performance of the trained model was evaluated using several metrics, including accuracy, precision, and recall. Additionally, in this research, a novel synthetic image generation tool, NephroNet, is developed on diffusion models that are used to generate original images of RCC surgical resection slides. Diffusion models are a class of generative models capable of synthesizing high-quality images from noise. Several diffusers such as Stable Diffusion, Dreambooth Text-to-Image, and Textual Inversion were trained on a dataset of RCC images and were used to generate a series of original images that resembled RCC surgical resection slides, all within the span of fewer than four seconds. The generated images were visually realistic and could be used for creating new training datasets, testing the performance of image analysis algorithms, and training medical professionals. NephroNet is provided as an open-source software package and contains files for data preprocessing, training, and visualization. Overall, this study demonstrates the potential of artificial intelligence and diffusion models for classifying and generating RCC images, respectively. These methods could be useful for improving the diagnosis and treatment of RCC and more.

Artificial intelligence models can help predict cardiotoxicity risk among patients with renal cell carcinoma treated with VEGF receptor inhibitors, according to study results. Integration of artificial intelligence (AI) models into electronic medical records can help oncologists and other members of the clinical care team identify those who may benefit from cardio-oncology monitoring and treatment, findings presented at International Kidney Cancer Symposium: North America showed. "Further studies comparing differences in outcomes between high-risk ... patients who were referred to cardio-oncology versus patients who were not referred are warranted," Hesham Yasin, MD, clinical fellow at Vanderbilt University Medical Center, and colleagues wrote. Tyrosine kinase inhibitors that target VEGF receptors are standard components of renal cell carcinoma treatment. These agents generally are effective and safe, but they can cause cardiotoxicity risk for an estimated 3% to 8% of patients, according to study background.