Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings

Head and neck surgery global outreach: Ethics, planning, and impact

Head and neck surgical oncology and reconstruction are uniquely suited to address burdens of disease in underserved areas. Since these efforts are not well known in our specialty, we sought to understand global outreach throughout our society of surgeons. Survey distributed to members of the American Head and Neck Surgery involved in international humanitarian head and neck surgical outreach trips. Thirty surgeons reported an average of seven trips to over 70 destinations. Identification of candidates, finances, on-site patient care, complications, long-term post-surgical care, ethics, and educational goals are reported. We report a success rate of 90% on 125 free flaps performed in these settings. The effort to answer the call for alleviating the global burden of surgical disease is strong within our specialty. There is a shared focus on humanitarian effort and teaching. Ethics of high resource surgeries such as free flap reconstruction remains controversial

Prognostic significance of non-HPV16 genotypes in oropharyngeal squamous cell carcinoma

OBJECTIVES: Recent studies have found that cases with oropharyngeal squamous cell carcinoma (OPSCC) positive for HPV16 genotype have better overall survival compared with cases positive for other HPV genotypes. We sought to further replicate these studies and determine if this relationship is modified by expression of p16 tumor suppressor protein. MATERIAL AND METHODS: We identified 238 OPSCC cases from the Carolina Head and Neck Cancer Study (CHANCE) study, a population based case-control study. Tumors that tested positive solely for HPV16 genotype and no other genotypes with PCR were classified as HPV16-positive. Tumors positive for any other high-risk HPV genotype were classified as non-HPV16-positive. Expression of p16 in the tumor was determined with immunohistochemistry. Follow-up time was calculated from the date of diagnosis to date of death or December 31, 2013. Overall survival was compared with the Kaplan-Meier curves and log-rank test. Hazard ratios (HR) adjusted for smoking, alcohol use, sex, race, and age was calculated with the Cox proportional hazard regression. RESULTS: Cases with HPV16-positive OPSCC had better overall survival than cases with non-HPV16-positive OPSCC (log-rank p-value: 0.010). When restricted to OPSCC cases positive for p16 expression, the same trend continued (log-rank p-value: 0.002). In the adjusted model, cases with non-HPV16-positive OPSCC had greater risk of death compared to cases with HPV16-positive tumors (HR: 1.92; 95% CI: 1.03, 3.60). CONCLUSIONS: This finding indicates that HPV genotyping carries valuable prognostic significance in addition to p16 status and future survival studies of OPSCC should take into account differing HPV genotypes

Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients

Radiomics is one such “big data” approach that applies advanced image refining/data characterization algorithms to generate imaging features that can quantitatively classify tumor phenotypes in a non-invasive manner. We hypothesize that certain textural features of oropharyngeal cancer (OPC) primary tumors will have statistically significant correlations to patient outcomes such as local control. Patients from an IRB-approved database dispositioned to (chemo)radiotherapy for locally advanced OPC were included in this retrospective series. Pretreatment contrast CT scans were extracted and radiomics-based analysis of gross tumor volume of the primary disease (GTVp) were performed using imaging biomarker explorer (IBEX) software that runs in Matlab platform. Data set was randomly divided into a training dataset and test and tuning holdback dataset. Machine learning methods were applied to yield a radiomic signature consisting of features with minimal overlap and maximum prognostic significance. The radiomic signature was adapted to discriminate patients, in concordance with other key clinical prognosticators. 465 patients were available for analysis. A signature composed of 2 radiomic features from pre-therapy imaging was derived, based on the Intensity Direct and Neighbor Intensity Difference methods. Analysis of resultant groupings showed robust discrimination of recurrence probability and Kaplan-Meier-estimated local control rate (LCR) differences between “favorable” and “unfavorable” clusters were noted

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

{Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants’ feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n=5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n=2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets towards clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings