Large-scale DNA organization is a prognostic marker of breast cancer survival

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer

INTRODUCTION

Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes.

AREAS COVERED

In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings.

EXPERT OPINION

The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.

The role of DNA image cytometry in screening oral potentially malignant lesions using brushings: A systematic review

It is believed that the majority of oral cancers develop from oral potentially malignant lesions (OPML). Though they can be easily detected during screening, risk stratification is difficult. During screening clinicians often find it difficult to distinguish OPMLs from benign lesions, and predicting OPML at risk of malignant transformation is particularly challenging. DNA aneuploidy has been known to be a marker of malignancy in a number of sites including the oral cavity. We performed a systematic review to evaluate the effectiveness of DNA-ICM using brushings in differentiating OPMLs from benign/inflammatory lesions during screening and in predicting malignant transformation. MEDLINE, Pubmed, EMBASE electronic databases were systematically searched using a combination of keywords and subject headings. A total of 11 articles satisfied our inclusion criteria. These studies reported a wide range of sensitivity (16-96.4%) and specificity (90-100%) due to the differences in study design, definitions of high risk or low risk lesions and DNA-ICM protocol used. No long-term longitudinal studies were identified to assess the role of DNA-ICM using brushings in predicting malignant transformation. No studies evaluated the role of DNA-ICM in community screening settings. A number of studies combined DNA-ICM with other techniques like cytology or argyrophilic nucleolar organizer region counts leading to improved test results. In spite of DNA aneuploidy being accepted as a marker of malignancy, there is limited evidence of DNA-ICM using brushings being successful as an adjunct oral cancer screening tool. Longitudinal studies and large community screening studies need to be undertaken to draw stronger conclusion.