Hearing Loss After Cisplatin-based Chemoradiotherapy for Locally Advanced Head and Neck Cancer: A Prospective Single-institution Study

BACKGROUND/AIM

A single-institution prospective study was conducted to evaluate hearing loss rate after intensity modulated radiotherapy with concomitant cisplatin-based chemotherapy (CRT) for locally advanced head and neck cancer and identify cochlear dosimetric parameters associated with hearing loss risk.

PATIENTS AND METHODS

Hearing assessment, patients' characteristics, tumor-related variables, and cochlear quantitative dosimetric factors for adults with locally advanced head and neck cancer treated with CRT were prospectively collected. Each patient repeated audiometry at baseline (pre-CRT), 1 month after CRT, and then every 3 to 6 months. For each cochlea minimum dose (D_min), mean dose (D_mean), and maximum dose (D_max) were extracted from treatment plans. Logistic analysis was used for multivariate modeling. The relation between cochlear dosimetric factors and significant hearing loss was also analyzed with receiver operating characteristic (ROC) curves.

RESULTS

Between January 2016 and December 2018, 35 patients (70 cochleae) were included. Most patients (n=29; 82.9%) had primary cancer in a low-risk region (oral cavity, oropharynx, larynx). All patients completed the programmed CRT. During follow-up, significant hearing loss was recorded in 13 cases (37.1%). The ROC areas for significant hearing loss in relation to D_min, D_mean, and D_max were 0.70, 0.66, and 0.66, respectively. A dose-dependent relationship was noted between cochlear D_min and significant hearing loss.

CONCLUSION

D_min <14.4 Gy is associated with reduced rates of significant hearing loss after concomitant cisplatin-based CRT in patients with locally advanced head and neck cancer.

Prediction of Recurrence by Machine Learning in Salivary Gland Cancer Patients After Adjuvant (Chemo)Radiotherapy

BACKGROUND/AIM

To investigate survival outcomes and recurrence patterns using machine learning in patients with salivary gland malignant tumor (SGMT) undergoing adjuvant chemoradiotherapy (CRT).

PATIENTS AND METHODS

Consecutive SGMT patients were identified, and a data set included nine predictor variables and a dependent variable [disease-free survival (DFS) event] was standardized. The open-source R software was used. Survival outcomes were estimated by the Kaplan-Meier method. The random forest approach was used to select the important explanatory variables. A classification tree that optimally partitioned SGMT patients with different DFS rates was built.

RESULTS

In total, 54 SGMT patients were included in the final analysis. Five-year DFS was 62.1%. The top two important variables identified were pathologic node (pN) and pathologic tumor (pT). Based on these explanatory variables, patients were partitioned in three groups, including pN0, pT1-2 pN+ and pT3-4 pN+ with 26%, 38% and 75% probability of recurrence, respectively. Accordingly, 5-year DFS rates were 73.7%, 57.1% and 34.3%, respectively.

CONCLUSION

The proposed decision tree algorithm is an appropriate tool to partition SGMT patients. It can guide decision-making and future research in the SGMT field.