Evaluating different radiotherapy treatment plans, in terms of critical organ scoring index, conformity index, tumor control probability, and normal tissue complication probability calculations in early glottic larynx carcinoma

Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist

BACKGROUND AND PURPOSE

Machine learning (ML) is emerging as a feasible approach to optimize patients' care path in Radiation Oncology. Applications include autosegmentation, treatment planning optimization, and prediction of oncological and toxicity outcomes. The purpose of this clinically oriented systematic review is to illustrate the potential and limitations of the most commonly used ML models in solving everyday clinical issues in head and neck cancer (HNC) radiotherapy (RT).

MATERIALS AND METHODS

Electronic databases were screened up to May 2021. Studies dealing with ML and radiomics were considered eligible. The quality of the included studies was rated by an adapted version of the qualitative checklist originally developed by Luo et al. All statistical analyses were performed using R version 3.6.1.

RESULTS

Forty-eight studies (21 on autosegmentation, four on treatment planning, 12 on oncological outcome prediction, 10 on toxicity prediction, and one on determinants of postoperative RT) were included in the analysis. The most common imaging modality was computed tomography (CT) (40%) followed by magnetic resonance (MR) (10%). Quantitative image features were considered in nine studies (19%). No significant differences were identified in global and methodological scores when works were stratified per their task (i.e., autosegmentation).

DISCUSSION AND CONCLUSION

The range of possible applications of ML in the field of HN Radiation Oncology is wide, albeit this area of research is relatively young. Overall, if not safe yet, ML is most probably a bet worth making.

Recurrent/Metastatic Squamous Cell Carcinoma of the Head and Neck: A Big and Intriguing Challenge Which May Be Resolved by Integrated Treatments Combining Locoregional and Systemic Therapies

Squamous cell carcinoma of the head and neck (SCCHN) is a complex group of malignancies, posing several challenges to treating physicians. Most patients are diagnosed with a locally advanced disease and treated with strategies integrating surgery, chemotherapy, and radiotherapy. About 50% of these patients will experience a recurrence of disease. Recurrent/metastatic SCCHN have poor prognosis with a median survival of about 12 months despite treatments. In the last years, the strategy to manage recurrent/metastatic SCCHN has profoundly evolved. Salvage treatments (surgery or re-irradiation) are commonly employed in patients suffering from locoregional recurrences and their role has gained more and more importance in the last years. Re-irradiation, using some particularly fractionating schedules, has the dual task of reducing the tumor mass and eliciting an immune response against cancer (abscopal effect). In this review, we will analyze the main systemic and/or locoregional strategies aimed at facing the recurrent/metastatic disease, underlining the enormous importance of the multidisciplinary approach in these types of patients.