Reirradiation for Nasal Cavity or Paranasal Sinus Tumor—A Multi-Institutional Study

We evaluated the efficacy and toxicity of reirradiation of nasal cavity or paranasal sinus tumors. We collected and analyzed multi-institutional data of reirradiation cases. Seventy-eight patients with nasal or paranasal sinus tumors underwent reirradiation. The median survival time was 20 months with a medial follow-up of 10.7 months. The 2-year local control and overall survival rates were 43% and 44%, respectively. Tumor volume (≤25 cm3), duration between previous radiotherapy and reirradiation (≤12 months), histology (squamous cell carcinoma), male sex, and lymph node involvement were predisposing factors for poor survival. Distant metastasis was observed in 20 patients (25.6%). Grade ≥ 3 adverse events were observed in 22% of the patients, including five grade 4 (8.6%) cases and one grade 5 (1.2%) case. Tumor location adjacent to the optic pathway was a significant predisposing factor for grade ≥3 visual toxicity. Reirradiation of nasal and paranasal sinus tumors is feasible and effective. However, adverse events, including disease-related toxicities, were significant. Prognostic factors emerge from this study to guide multidisciplinary approaches and clinical trial designs.

Texture Analysis of Fat-Suppressed T2-Weighted Magnetic Resonance Imaging and Use of Machine Learning to Discriminate Nasal and Paranasal Sinus Small Round Malignant Cell Tumors

ObjectiveWe used texture analysis and machine learning (ML) to classify small round cell malignant tumors (SRCMTs) and Non-SRCMTs of nasal and paranasal sinus on fat-suppressed T2 weighted imaging (Fs-T2WI).MaterialsPreoperative MRI scans of 164 patients from 1 January 2018 to 1 January 2021 diagnosed with SRCMTs and Non-SRCMTs were included in this study. A total of 271 features were extracted from each regions of interest. Datasets were randomly divided into two sets, including a training set (∼70%) and a test set (∼30%). The Pearson correlation coefficient (PCC) and principal component analysis (PCA) methods were performed to reduce dimensions, and the Analysis of Variance (ANOVA), Kruskal-Wallis (KW), and Recursive Feature Elimination (RFE) and Relief were performed for feature selections. Classifications were performed using 10 ML classifiers. Results were evaluated using a leave one out cross-validation analysis.ResultsWe compared the AUC of all pipelines on the validation dataset with FeAture Explorer (FAE) software. The pipeline using a PCC dimension reduction, relief feature selection, and gaussian process (GP) classifier yielded the highest area under the curve (AUC) using 15 features. When the “one-standard error” rule was used, FAE also produced a simpler model with 13 features, including S(5,-5)SumAverg, S(3,0)InvDfMom, Skewness, WavEnHL_s-3, Horzl_GlevNonU, Horzl_RLNonUni, 135dr_GlevNonU, WavEnLL_s-3, Teta4, Teta2, S(5,5)DifVarnc, Perc.01%, and WavEnLH_s-2. The AUCs of the training/validation/test datasets were 1.000/0.965/0.979, and the accuracies, sensitivities, and specificities were 0.890, 0.880, and 0.920, respectively. The best algorithm was GP whose AUCs of the training/validation/test datasets by the two-dimensional reduction methods and four feature selection methods were greater than approximately 0.800. Especially, the AUCs of different datasets were greater than approximately 0.900 using the PCC, RFE/Relief, and GP algorithms.ConclusionsWe demonstrated the feasibility of combining artificial intelligence and the radiomics from Fs-T2WI to differentially diagnose SRCMTs and Non-SRCMTs. This non-invasive approach could be very promising in clinical oncology.