Comparison of patient stratification by computed tomography radiomics and hypoxia positron emission tomography in head-and-neck cancer radiotherapy

Computer Vision-Radiomics & Pathognomics

The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.

Systematic review and meta-analysis of the prognostic value of 18F-Fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) and/or computed tomography (CT)-based radiomics in head and neck cancer

AIM

Radiomics involves the extraction of quantitative data from medical images to facilitate the diagnosis, prognosis, and staging of tumors. This study provides a comprehensive overview of the efficacy of radiomics in prognostic applications for head and neck cancer (HNC) in recent years. It undertakes a systematic review of prognostic models specific to HNC and conducts a meta-analysis to evaluate their predictive performance.

MATERIALS AND METHODS

This study adhered rigorously to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for literature searches. The literature databases, including PubMed, Embase, Cochrane, and Scopus were systematically searched individually. The methodological quality of the incorporated studies underwent assessment utilizing the radiomics quality score (RQS) tool. A random-effects meta-analysis employing the Harrell concordance index (C-index) was conducted to evaluate the performance of all radiomics models.

RESULTS

Among the 388 studies retrieved, 24 studies encompassing a total of 6,978 cases were incorporated into the systematic review. Furthermore, eight studies, focusing on overall survival as an endpoint, were included in the meta-analysis. The meta-analysis revealed that the estimated random effect of the C-index for all studies utilizing radiomics alone was 0.77 (0.71-0.82), with a substantial degree of heterogeneity indicated by an I2 of 80.17%.

CONCLUSIONS

Based on this review, prognostic modeling utilizing radiomics has demonstrated enhanced efficacy for head and neck cancers; however, there remains room for improvement in this approach. In the future, advancements are warranted in the integration of clinical parameters and multimodal features, balancing multicenter data, as well as in feature screening and model construction within this field.

Automatic evaluation of contours in radiotherapy planning utilising conformity indices and machine learning

BACKGROUND AND PURPOSE

Peer-review of Target Volume (TV) and Organ at Risk (OAR) contours in radiotherapy planning are typically conducted visually; this can be time consuming and subject to interobserver variation. This study investigated automatic evaluation of contouring using conformity indices and supervised machine learning.

METHODS

A total of 393 contours from 253 Stereotactic Ablative Body Radiotherapy (SABR) benchmark cases (adrenal gland, liver, pelvic lymph node and spine), delineated by 132 clinicians from 25 centres, were visually evaluated for conformity against gold standard contours. Contours were scored as "pass" or "fail" on visual peer review and six Conformity Indices (CIs) were applied. CI values were mapped to pass/fail scores for each contour and used to train supervised machine learning models. A 5-fold cross validation method was employed to determine the predictive accuracies of each model.

RESULTS

The stomach structure produced models with the highest predictive accuracy overall (96% using Support Vector Machine and Ensemble models), whilst the liver GTV produced models with the lowest predictive accuracy (76% using Logistic Regression). Predictive accuracies across all models ranged from 68-96% (68-87% for TV and 71-96% for OARs).

CONCLUSIONS

Although a final visual review by an experienced clinician is still required, the automatic contour evaluation method could reduce the time for benchmark case reviews by identifying gross contouring errors. This method could be successfully implemented to support departmental training and the continuous assessment of outlining for clinical staff in the peer-review process, to reduce interobserver variability in contouring and improve interpretation of radiological anatomy.