Prognostication Of Treatment Response Research Articles

Radiomics in ophthalmology: a systematic review.

Radiomics holds great potential in medical image analysis for various ophthalmic diseases. In recent times, there have been numerous endeavors in this area of research. This systematic review aims to provide a comprehensive assessment of the strengths and limitations of radiomics in ophthalmology. Conforming to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, we conducted a systematic review with a pre-registered protocol (PROSPERO: CRD42023446317). We explored the PubMed, Embase, and Cochrane databases for original studies on this topic and made a comprehensive descriptive integration. Furthermore, the included studies underwent quality assessment by the radiomics quality score (RQS). A total of 41 articles from an initial search of 227 studies were finally selected for further analysis. These articles included research across five disease categories and covered seven imaging modalities. The radiomics models demonstrated robust performance, with area under the curve (AUC) values mostly falling within 0.7-1.0. The moderate RQS (mean score: 11.17/36) indicated that most studies were retrospectively, single-center analyses without external validation. Radiomics holds promising utility in the field of ophthalmology, assisting diagnosis, early-stage screening, and prognostication of treatment response. Artificial intelligence algorithms significantly contribute to the construction of radiomics models in ophthalmology. This study highlights the strengths and challenges of radiomics in ophthalmology and suggests potential avenues for future improvement. Radiomics represents a valuable approach for generating innovative imaging markers, enhancing efficiency in clinical diagnosis and treatment, and aiding decision-making in clinical contexts of many ophthalmic diseases, thereby improving overall patient prognosis. Radiomics has attracted extensive attention in the field of ophthalmology. Articles included five disease categories over seven imaging modalities, consistently yielding AUCs mostly above 0.7. Current research has few prospective and multi-center studies, underlining the necessity for future high-quality studies.

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment.

Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical "how-to" guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.

Extramural venous invasion (EMVI) revisited: a detailed analysis of various characteristics of EMVI and their role as a predictive imaging biomarker in the neoadjuvant treatment response in rectal cancer.

To assess whether size, diameter, and large vein involvement of MR-detected extramural venous invasion (MR-EMVI) have an impact on neoadjuvant therapy response in rectal adenocarcinoma. 57 patients with locally advanced rectal adenocarcinoma scanned with MRI before and after neoadjuvant therapy were included. Two abdominal radiologists evaluated the images with special emphasis on EMVI, on initial staging and after neoadjuvant treatment. The sensitivity and specificity of MRI for detection of rest EMVI were determined. The association of various MR-EMVI characteristics including number, size, and main vein involvement with treatment response was investigated. In subjects with discordance of radiology and pathology, elastin stain was performed, and images and slides were re-evaluated on site with a multidisciplinary approach. At initial evaluation, 17 patients were MR-EMVI negative (29.8%) and 40 were MR-EMVI positive (70.2%). Complete/near-complete responders had less number (mean 1.45) and smaller diameter of MR-EMVI (mean 1.8mm), when compared with partial responders (2.54 and 3.3mm; p < 0.005). The sensitivity of MRI for rest EMVI detection was high, specificity was moderate, and in one patient elastin stain changed the final decision. In five patients with rest MR-EMVI positivity, carcinoma histopathologically had a distinctive serpiginous perivascular spread, growing along the track of vascular bundle, although it did not appear in intravascular spaces. This study demonstrates that not only the presence, but also size and number of EMVI that may be significant clinically and thus these parameters also ought to be incorporated to the MRI evaluation and prognostication of treatment response. From pathology perspective, tumors growing alongside major vessels may also reflect EMVI even if they are not demonstrably "intravascular."

DETECT: Development of Technologies for Early HCC Detection

DETECT: Development of Technologies for Early HCC Detection

14 speaker: Tumour Biology-Guided Prognostication of Treatment Response

14 speaker: Tumour Biology-Guided Prognostication of Treatment Response