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Background Evaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a novel tumour biomarker in predicting overall survival of OSCC patients treated by concurrent chemoradiotherapy. Methods Independent patient cohorts from two hospitals were included for training (n = 87) and validation (n = 46). Radiomics features were extracted from sub-regions clustered from patients' tumour regions using K-means method. The LASSO regression for ‘Cox’ method was used for feature selection. The survival prediction model was constructed based on the sub-region radiomics features using the Cox proportional hazards model. The clinical and biological significance of radiomics features were assessed by correlation analysis of clinical characteristics and copy number alterations(CNAs) in the validation dataset. Findings The overall survival prediction model combining with seven sub-regional radiomics features was constructed. The C-indexes of the proposed model were 0.729 (0.656–0.801, 95% CI) and 0.705 (0.628–0.782, 95%CI) in the training and validation cohorts, respectively. The 3-year survival receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.811 (0.670–0.952, 95%CI) in training and 0.805 (0.638–0.973, 95%CI) in validation. The correlation analysis showed a significant correlation between radiomics features and CNAs. Interpretation The proposed sub-regional radiomics model could predict the overall survival risk for patients with OSCC treated by definitive concurrent chemoradiotherapy. Fund This work was supported by the Zhejiang Provincial Foundation for Natural Sciences, National Natural Science Foundation of China.

This study aimed to design automated volumetric-modulated arc therapy (VMAT) plans in Pinnacle auto-planning and compare it with manual plans for patients with lower thoracic esophageal cancer (EC). Thirty patients with lower thoracic EC were randomly selected for replanning VMAT plans using auto-planning in Pinnacle treatment planning system (TPS) version 9.10. Historical plans of these patients were then compared. Dose-volume histogram (DVH) statistics, dose uniformity, and dose homogeneity were analyzed to evaluate treatment plans. Auto-planning was superior in terms of conformity index (CI) and homogeneity index (HI) for planning target volume (PTV), significantly improving 8.2% (p = 0.013) and 25% (p = 0.007) compared with manual planning, respectively, and decreasing dose of heart and liver irradiated by 20 to 40 Gy and 5 to 30 Gy, respectively (p < 0.05). Meanwhile, auto-planning further reduced the maximum dose (Dmax) of spinal cord by 6.9 Gy compared with manual planning (p = 0.000). Additionally, manual planning showed the significantly lower low-dose volume (V5) for the lung (p = 0.005). For auto-planning, the V5 of the lung was significantly associated with the relative volume index (the volume ratio of PTV to the lung), and the correlation coefficient (R) and p-value were 0.994 and 0.000. Pinnacle auto-planning achieved superior target conformity and homogeneity and similar target coverage compared with historical manual planning. Most of organs at risk (OARs) sparing was significantly improved by auto-planning except for the V5 of the lung, and the low dose distribution was highly associated with PTV volume and lung volume in auto-planning.

Purpose/Objective(s) The precise radiomics analysis on thoracic 4DCT data is easily compromised by the respiratory motion and CT scan parameter setting, thus leading to the risk of overfitting and/or misinterpretation of data in AI-enabled therapeutic model building. In this study, we investigated the impact of respiratory amplitudes, frequencies and CT scan pitch settings within the thoracic 4DCT scan on robust radiomics feature selection. Materials/Methods A Three-dimensional QUSARTM lung tumor phantom was used to simulate different respiratory amplitudes and frequencies along with different CT scan pitch settings. A total of 43 tumor respiratory patterns extracted from 43 patients with non-small cell lung cancer were used to drive the QUSARTM lung tumor phantom to mimic the human tumor motion. The 4DCT images of the QUSARTM lung tumor phantom with different respiratory patterns and different CT scan pitch setups were acquired for radiomics feature extraction. A static high-quality CT images of the phantom acquired were also used as a reference for radiomics feature extraction. The range of respiratory amplitudes was mimicked at 3mm at left and right (LR) and anterior and posterior (AP) directions and 3mm - 15 mm at the superior and inferior (SI) direction with an interval of 2 mm. The respiratory frequencies were set at 10, 11, 12, 13, 14, 15 and 20 beats per minute (BPMs), respectively. The CT scan pitches were set at 0.025, 0.048, 0.071, 0.93, 0.108, 0.14, 0.16, 0.18, 0.21, 0.23, and 0.25, respectively, which was based on a procedure described in Med. Phys. 30(1):88-97. The pairwise Concordance Correlation Coefficient (CCC) was used to determine the robustness of radiomics feature extraction via comparing the agreement in feature values between 1766 radiomics features extracted from each image acquired under different combinations of respiratory amplitudes and frequencies and CT scan pitches of 4DCT and those extracted from the static CT images. Results (1) When the respiratory amplitudes were at 3, 5, 7, 9, 12 and 15mm in the SI direction, the maximum CCC index could be achieved at the reconstructed 4DCT phase images of 60%, 70%, 30%, 20%, 60%~70% and 10%, respectively. Under these six amplitudes, the maximum intensity projection (MIP) and average intensity projection (AIP) images reconstructed show mean CCC values of 0.778 and 0.609, respectively, in pairwise radiomics feature extraction comparison between 4DCT and static CT. (2) When the respiratory amplitude was set at 12 mm in the SI direction, the maximum CCC index could be consistently achieved at the reconstructed 4DCT phase of 90% for the seven respiratory frequencies of 10, 11, 12, 13, 14, 15 and 20 BPMs, respectively. Under these respiratory states, the MIP and AIP images reconstructed show mean CCC values of 0.702 and 0.562, respectively. (3) When the respiratory amplitude was set at 12 mm and the respiratory frequency was set at 13 BPM, the maximum CCC index could be obtained at the reconstructed 4DCT phase of 90% for all scan pitches used except the 0% phase which was obtained at the pitch setting of 0.048. Under these CT scan pitch settings, the MIP and AIP images reconstructed show mean CCC values of 0.558 and 0.782, respectively. (4) The total number of robust features were 50, 34 and 35 with different respiratory amplitudes and phases and CT scanning pitch used (CCC values ≥ 0.99). Conclusion In 4DCT, the respiratory amplitude, frequency and CT scan pitch are three limiting factors that greatly affect the robustness of radiomics feature extraction. The reconstructed 4DCT phases with better robustness along with suitable respiratory amplitude, frequency and CT scan pitch determined could be used to guide the breathing training for patients with lung cancer for radiation therapy to improve the robust radiomics feature extraction process.