Quantification of the spatial distribution of primary tumors in the lung to develop new prognostic biomarkers for locally advanced NSCLC.

The anatomical location and extent of primary lung tumors have shown prognostic value for overall survival (OS). However, its manual assessment is prone to interobserver variability. This study aims to use data driven identification of image characteristics for OS in locally advanced non-small cell lung cancer (NSCLC) patients. Five stage IIIA/IIIB NSCLC patient cohorts were retrospectively collected. Patients were treated either with radiochemotherapy (RCT): RCT1* (n = 107), RCT2 (n = 95), RCT3 (n = 37) or with surgery combined with radiotherapy or chemotherapy: S1* (n = 135), S2 (n = 55). Based on a deformable image registration (MIM Vista, 6.9.2.), an in-house developed software transferred each primary tumor to the CT scan of a reference patient while maintaining the original tumor shape. A frequency-weighted cumulative status map was created for both exploratory cohorts (indicated with an asterisk), where the spatial extent of the tumor was uni-labeled with 2 years OS. For the exploratory cohorts, a permutation test with random assignment of patient status was performed to identify regions with statistically significant worse OS, referred to as decreased survival areas (DSA). The minimal Euclidean distance between primary tumor to DSA was extracted from the independent cohorts (negative distance in case of overlap). To account for the tumor volume, the distance was scaled with the radius of the volume-equivalent sphere. For the S1 cohort, DSA were located at the right main bronchus whereas for the RCT1 cohort they further extended in cranio-caudal direction. In the independent cohorts, the model based on distance to DSA achieved performance: AUCRCT2 [95% CI] = 0.67 [0.55-0.78] and AUCRCT3 = 0.59 [0.39-0.79] for RCT patients, but showed bad performance for surgery cohort (AUCS2 = 0.52 [0.30-0.74]). Shorter distance to DSA was associated with worse outcome (p = 0.0074). In conclusion, this explanatory analysis quantifies the value of primary tumor location for OS prediction based on cumulative status maps. Shorter distance of primary tumor to a high-risk region was associated with worse prognosis in the RCT cohort.

Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging.

BACKGROUND: Radiomics is a promising tool for identifying imaging-based biomarkers. Radiomics-based models are often trained on single-institution datasets; however, multi-centre imaging datasets are preferred for external generalizability owing to the influence of inter-institutional scanning differences and acquisition settings. The study aim was to determine the value of preselection of robust radiomic features in routine clinical positron emission tomography (PET) images to predict clinical outcomes in locally advanced non-small cell lung cancer (NSCLC). METHODS: A total of 1404 primary tumour radiomic features were extracted from pre-treatment [18F]fluorodeoxyglucose (FDG)-PET scans of stage IIIA/N2 or IIIB NSCLC patients using a training cohort (n = 79; prospective Swiss multi-centre randomized phase III trial SAKK 16/00; 16 centres) and an internal validation cohort (n = 31; single centre). Robustness studies investigating delineation variation, attenuation correction and motion were performed (intraclass correlation coefficient threshold > 0.9). Two 12-/24-month event-free survival (EFS) and overall survival (OS) logistic regression models were trained using standardized imaging: (1) with robust features alone and (2) with all available features. Models were then validated using fivefold cross-validation, and validation on a separate single-centre dataset. Model performance was assessed using area under the receiver operating characteristic curve (AUC). RESULTS: Robustness studies identified 179 stable features (13%), with 25% stable features for 3D versus 4D acquisition, 31% for attenuation correction and 78% for delineation. Univariable analysis found no significant robust features predicting 12-/24-month EFS and 12-month OS (p value > 0.076). Prognostic models without robust preselection performed well for 12-month EFS in training (AUC = 0.73) and validation (AUC = 0.74). Patient stratification into two risk groups based on 12-month EFS was significant for training (p value = 0.02) and validation cohorts (p value = 0.03). CONCLUSIONS: A PET-based radiomics model using a standardized, multi-centre dataset to predict EFS in locally advanced NSCLC was successfully established and validated with good performance. Prediction models with robust feature preselection were unsuccessful, indicating the need for a standardized imaging protocol.

SAKK 16/14: Durvalumab in Addition to Neoadjuvant Chemotherapy in Patients With Stage IIIA(N2) Non-Small-Cell Lung Cancer-A Multicenter Single-Arm Phase II Trial.

PURPOSE: For patients with resectable stage IIIA(N2) non-small-cell lung cancer, neoadjuvant chemotherapy with cisplatin and docetaxel followed by surgery resulted in a 1-year event-free survival (EFS) rate of 48% in the SAKK 16/00 trial and is an accepted standard of care. We investigated the additional benefit of perioperative treatment with durvalumab. METHODS: Neoadjuvant treatment consisted of three cycles of cisplatin 100 mg/m2 and docetaxel 85 mg/m2 once every 3 weeks followed by two doses of durvalumab 750 mg once every 2 weeks. Durvalumab was continued for 1 year after surgery. The primary end point was 1-year EFS. The hypothesis for statistical considerations was an improvement of 1-year EFS from 48% to 65%. RESULTS: Sixty-eight patients were enrolled, 67 were included in the full analysis set. Radiographic response rate was 43% (95% CI, 31 to 56) after neoadjuvant chemotherapy and 58% (95% CI, 45 to 71) after sequential neoadjuvant immunotherapy. Fifty-five patients were resected, of which 34 (62%) achieved a major pathologic response (MPR; ≤ 10% viable tumor cells) and 10 (18%) among them a complete pathologic response. Postoperative nodal downstaging (ypN0-1) was observed in 37 patients (67%). Fifty-one (93%) resected patients had an R0 resection. There was no significant effect of pretreatment PD-L1 expression on MPR or nodal downstaging. The 1-year EFS rate was 73% (two-sided 90% CI, 63 to 82). Median EFS and overall survival were not reached after 28.6 months of median follow-up. Fifty-nine (88%) patients had an adverse event grade ≥ 3 including two fatal adverse events that were judged not to be treatment-related. CONCLUSION: The addition of perioperative durvalumab to neoadjuvant chemotherapy in patients with stage IIIA(N2) non-small-cell lung cancer is safe and exceeds historical data of chemotherapy alone with a high MPR and an encouraging 1-year EFS rate of 73%.