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This review furnishes an exhaustive analysis of the latest advancements in deep learning techniques applied to whole slide images (WSIs) in the context of cancer prognosis, focusing specifically on publications from 2019 through 2023. The swiftly maturing field of deep learning, in combination with the burgeoning availability of WSIs, manifests significant potential in revolutionizing the predictive modeling of cancer prognosis. In light of the swift evolution and profound complexity of the field, it is essential to systematically review contemporary methodologies and critically appraise their ramifications. This review elucidates the prevailing landscape of this intersection, cataloging major developments, evaluating their strengths and weaknesses, and providing discerning insights into prospective directions. In this paper, a comprehensive overview of the field aims to be presented, which can serve as a critical resource for researchers and clinicians, ultimately enhancing the quality of cancer care outcomes. This review's findings accentuate the need for ongoing scrutiny of recent studies in this rapidly progressing field to discern patterns, understand breakthroughs, and navigate future research trajectories. [ABSTRACT FROM AUTHOR]

Current approaches to breast cancer therapy include neoadjuvant systemic therapy (NST). The efficacy of NST is measured by pathologic complete response (pCR). A patient who attains pCR has significantly enhanced disease-free survival progress. The accurate prediction of pCR in response to a given treatment regimen could increase the likelihood of achieving pCR and prevent toxicities caused by treatments that are not effective. Th early prediction of response to NST can increase the likelihood of survival and help with decisions regarding breast-conserving surgery. An automated NST prediction framework that is able to precisely predict which patient undergoing NST will achieve a pathological complete response (pCR) at an early stage of treatment is needed. Here, we propose an end-to-end efficient multimodal spatiotemporal deep learning framework (deep-NST) framework to predict the outcome of NST prior or at an early stage of treatment. The deep-NST model incorporates imaging data captured at different timestamps of NST regimens, a tumor's molecular data, and a patient's demographic data. The efficacy of the proposed work is validated on the publicly available ISPY-1 dataset, in terms of accuracy, area under the curve (AUC), and computational complexity. In addition, seven ablation experiments were carried out to evaluate the impact of each design module in the proposed work. The experimental results show that the proposed framework performs significantly better than other recent methods. [ABSTRACT FROM AUTHOR]

Local Hyperthermia treatment (LHT) holds great promise as an adjuvant method in combating breast cancer. In LHT treatment, cancerous tissue is exposed to supraphysiological temperature in order to destroy the tissue directly or improve their susceptibility to other treatment regimes. To observe the progression of tissue necrosis during LHT treatment, a temperature elevation monitoring system is important. Single beam ultrasound (SBUS) is a convenient, non-invasive, and radiation free method that is relatively simple compared to other imaging modalities. Therefore, this study investigates the sensitivity of SBUS towards microstructural tissue changes during LHT treatment. Ex-vivo experiments are conducted on both normal and pathological breast tissues harvested from carcinogenic induced animal models. These tissue samples are exposed to high temperatures ranging from 37oC to 55oC. Different sets of samples were used for each temperature range. For each temperature group, 11 samples were used and tested. Protein concentrations in all the samples are then quantitatively measured for in-depth correlation and sensitivity analysis. Microscopic histological analysis and comparison with B-Mode ultrasound are also carried out for verification purposes. Result shows that there is a significant correlation between attenuation level and total protein concentration in pathological tissues with an observed value of 0.617 and p-value of 0.0001. Histological analysis indicates that cellular-level damage seen in pathological tissue samples is much more significant compared to normal tissues. Comparison with B-Mode ultrasound shows consistent mean grey scale and attenuation trends during LHT treatment, which supported the findings obtained using the SBUS method. [ABSTRACT FROM AUTHOR]

Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors, particularly for immunotherapy. However, the methods to assess such properties are expensive, time-consuming, and often not routinely performed. Applying machine learning to H&E images can provide a more cost-effective screening method. Dozens of studies over the last few years have demonstrated that a variety of molecular biomarkers can be predicted from H&E alone using the advancements of deep learning: molecular alterations, genomic subtypes, protein biomarkers, and even the presence of viruses. This article reviews the diverse applications across cancer types and the methodology to train and validate these models on whole slide images. From bottom-up to pathologist-driven to hybrid approaches, the leading trends include a variety of weakly supervised deep learning-based approaches, as well as mechanisms for training strongly supervised models in select situations. While results of these algorithms look promising, some challenges still persist, including small training sets, rigorous validation, and model explainability. Biomarker prediction models may yield a screening method to determine when to run molecular tests or an alternative when molecular tests are not possible. They also create new opportunities in quantifying intratumoral heterogeneity and predicting patient outcomes. [ABSTRACT FROM AUTHOR]

Background The treatment responses of immune checkpoint inhibitors in metastatic renal cell carcinoma (mRCC) vary, requiring reliable prognostic biomarkers. We assessed the prognostic ability of computed tomography (CT) texture analysis in patients with mRCC treated with programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors. Materials and Methods Sixty-eight patients with mRCC treated with PD-1/PD-L1 inhibitors between 2012 and 2019 were revaluated. Using baseline and first follow-up CT, baseline and follow-up texture models were developed to predict overall survival (OS) and progression-free survival (PFS) using least absolute shrinkage and selection operator Cox-proportional hazards analysis. Patients were divided into high-risk or low-risk group, and the survival difference was assessed using Kaplan-Meier and log-rank test. Multivariable Cox models were constructed by including only the clinical variables (clinical models) and by combining the clinical variables and the texture models (combined clinical-texture models), and their predictive performance was evaluated using Harrell's C-index. Results The baseline texture models distinguished longer- and shorter-term survivors for both OS (median, 60.1 vs. 17.0 months; P =.048) and PFS (5.2 vs. 2.8 months; P =.003). The follow-up texture models distinguished longer- and shorter-term overall survivors (40.3 vs. 15.2 months; P =.008) but not for PFS (5.0 vs. 3.6 months; P =.25). The combined clinical-texture model outperformed the clinical model in both predicting the OS (C-index, 0.70 vs. 0.63; P =.03) and PFS (C-index, 0.63 vs. 0.55; P =.04). Conclusion CT texture analysis performed at baseline and early after starting PD-1/PD-L1 inhibitors is associated with clinical outcomes of patients with mRCC. [ABSTRACT FROM AUTHOR]

The treatment of breast cancer is often complicated by lymphedema of the upper limbs. Standard lymphedema evaluation methodologies are not able to measure tissue fibrosis. The ultrasound aspects related to tissue microstructures of lymphedema are neglected in clinical evaluations. The objective of this study was to identify and measure the degree of impairment, topography, and biophysical alterations of subcutaneous lymphedema tissue secondary to the treatment of breast cancer by ultrasonography. Forty-two women at a mean age of 58 (±9.7) years, with unilateral lymphedema due to breast cancer treatment, were evaluated. The upper limbs were divided into affected (affected by lymphedema) and control (contralateral limb). Each limb was subdivided into seven areas, defined by perimetry, evaluated in pairs. The biophysical characteristics thickness, entropy, and echogenicity were evaluated by ultrasonography. The results showed a significant difference in the echogenicity and thickness variables between the affected and unaffected upper limb, in all the extent of the upper limb, while entropy showed no significant difference. The findings indicate that the data presented were consistent both in identifying and measuring the degree of impairment and biophysical changes in the subcutaneous tissue of lymphedema secondary to the treatment of breast cancer. [ABSTRACT FROM AUTHOR]