Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics

Abstract

In this study, we investigate the use of imaging feature-based outcomes research (“radiomics”) combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified. From pre-treatment PET scans, 24 representative imaging features of FDG-avid disease regions were extracted. Using machine learning-based feature selection methods, multiparameter logistic regression models were built incorporating clinical factors and imaging features. All model building methods were tested by cross validation to avoid overfitting, and final outcome models were validated on an independent dataset from a collaborating institution. Multiparameter models were statistically significant on 5-fold cross validation with the area under the receiver operating characteristic curve (AUC)=0.65 (p=0.004), 0.73 (p=0.026), and 0.66 (p=0.015) for ACM, LF, and DM, respectively. The model for LF retained significance on the independent validation cohort with AUC=0.68 (p=0.029) whereas the models for ACM and DM did not reach statistical significance, but resulted in comparable predictive power to the 5-fold cross validation with AUC=0.60 (p=0.092) and 0.65 (p=0.062), respectively. In the largest study of its kind to date, predictive features including increasing metabolic tumor volume, increasing image heterogeneity, and increasing tumor surface irregularity significantly correlated to mortality, LF, and DM on 5-fold cross validation in a relatively uniform single-institution cohort. The LF model also retained significance in an independent population.

A prediction model of surgical site infection after instrumented thoracolumbar spine surgery in adults

“…The model has an AUC of 0.72 (95% CI 0.65-0.79). This is considered to be moderate and is comparable to prediction models from other clinical disciplines with an AUC range from 0.54 to 0.73 [22,23]. Bear in mind that the model is used for prediction of future events, compared to diagnostic models that estimate the probability of the presence or absence of an outcome in the present time.…”

Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma

“…Such features correlate with clinical outcome and convey medically meaningful information describing tumor heterogeneity, microenvironment, pathophysiology, and mutational burden [13,18,19]. While prior studies demonstrated prognostic value of radiomics biomarkers in head and neck cancers [15,16,[20][21][22][23][24][25][26][27][28], none have incorporated or compared the AJCC 8th edition staging scheme in OPSCC survival modelling and stratification. In this study, we explored the potential added value of radiomics biomarkers in prognostication of PFS and OS-beyond the AJCC staging scheme-in a multi-institutional cohort.…”

A critical appraisal of the clinical applicability and risk of bias of the predictive models for mortality and recurrence in patients with oropharyngeal cancer: Systematic review

“…With respect to Participants , the three most recent studies used high‐stage patients and the rest considered any degree of severity. It should also be noted that four studies used external validation cohorts after having previously internally validated the models developed . Finally, one of the studies did not include treatment as a candidate predictor .…”