Promising Candidate Prognostic Biomarkers in [18F]FDG PET Images: Evaluation in Independent Cohorts of Non-Small Cell Lung Cancer Patients.

The normalized distances from the hot spot of radiotracer uptake (SUVmax) to the tumor centroid (NHOC) and to the tumor perimeter (NHOP) have recently been suggested as novel PET features reflecting tumor aggressiveness. These biomarkers characterizing the shift of SUVmax toward the lesion edge during tumor progression have been shown to be prognostic factors in breast and non-small cell lung cancer (NSCLC) patients. We assessed the impact of imaging parameters on NHOC and NHOP, their complementarity to conventional PET features, and their prognostic value for advanced-NSCLC patients. Methods: This retrospective study investigated baseline [18F]FDG PET scans: cohort 1 included 99 NSCLC patients with no treatment-related inclusion criteria (robustness study); cohort 2 included 244 NSCLC patients (survival analysis) treated with targeted therapy (93), immunotherapy (63), or immunochemotherapy (88). Although 98% of patients had metastases, radiomic features including SUVs were extracted from the primary tumor only. NHOCs and NHOPs were computed using 2 approaches: the normalized distance from the localization of SUVmax or SUVpeak to the tumor centroid or perimeter. Bland-Altman analyses were performed to investigate the impact of both spatial resolution (comparing PET images with and without gaussian postfiltering) and image sampling (comparing 2 voxel sizes) on feature values. The correlation of NHOCs and NHOPs with other features was studied using Spearman correlation coefficients (r). The ability of NHOCs and NHOPs to predict overall survival (OS) was estimated using the Kaplan-Meier method. Results: In cohort 1, NHOC and NHOP features were more robust to image filtering and to resampling than were SUVs. The correlations were weak between NHOCs and NHOPs (r ≤ 0.45) and between NHOCs or NHOPs and any other radiomic features (r ≤ 0.60). In cohort 2, the patients with short OS demonstrated higher NHOCs and lower NHOPs than those with long OS. NHOCs significantly distinguished 2 survival profiles in patients treated with immunotherapy (log-rank test, P < 0.01), whereas NHOPs stratified patients regarding OS in the targeted therapy (P = 0.02) and immunotherapy (P < 0.01) subcohorts. Conclusion: Our findings suggest that even in advanced NSCLC patients, NHOC and NHOP features pertaining to the primary tumor have prognostic potential. Moreover, these features appeared to be robust with respect to imaging protocol parameters and complementary to other radiomic features and are now available in LIFEx software to be independently tested by others.

Real-World Survival Impact of New Treatment Strategies for Lung Cancer: A 2000-2020 French Cohort.

Over the past 20 years, several innovative therapies have been implemented in the treatment of lung cancer that have had reported survival benefits in clinical trials. Whether these improvements translate into the clinic setting has not been studied yet. We retrospectively analyzed all patients consecutively treated at Institute Curie for metastatic lung cancer. Diagnosis date was used to define three periods, based on the approvals of novel treatment strategies in the first-line setting, including targeted therapies in 2010 and immunotherapy in 2018. Endpoints included Overall survival (OS), survival rate of 2 years and 5 years, and a conditional survival rate of 2 years (if still alive at 6 months from treatment initiation). A total of 673 patients were identified for Period 1-2000 to 2009, 752 for Period 2-2010 to 2017, and 768 for Period 3-2018 to 2020. Median OS in the whole cohort was 11.1, 15.5, and 16.2 months, respectively. Median OS for patients with NSCLC or SCLC was 11.2, 17.2, and 18.2 months, or 10.9, 11.7, and 11.2 months, respectively. The two-year conditional survival was more favorable for NSCLC than SCLC patients. Outcomes were statistically higher for women as compared to men in all periods and all subgroups. Survival of patients with metastatic lung cancer has improved over the past 20 years, mostly in NSCLC, along with the implementation of novel treatment strategies.

Consore: A Powerful Federated Data Mining Tool Driving a French Research Network to Accelerate Cancer Research.

BACKGROUND: Real-world data (RWD) related to the health status and care of cancer patients reflect the ongoing medical practice, and their analysis yields essential real-world evidence. Advanced information technologies are vital for their collection, qualification, and reuse in research projects. METHODS: UNICANCER, the French federation of comprehensive cancer centres, has innovated a unique research network: Consore. This potent federated tool enables the analysis of data from millions of cancer patients across eleven French hospitals. RESULTS: Currently operational within eleven French cancer centres, Consore employs natural language processing to structure the therapeutic management data of approximately 1.3 million cancer patients. These data originate from their electronic medical records, encompassing about 65 million medical records. Thanks to the structured data, which are harmonized within a common data model, and its federated search tool, Consore can create patient cohorts based on patient or tumor characteristics, and treatment modalities. This ability to derive larger cohorts is particularly attractive when studying rare cancers. CONCLUSIONS: Consore serves as a tremendous data mining instrument that propels French cancer centres into the big data era. With its federated technical architecture and unique shared data model, Consore facilitates compliance with regulations and acceleration of cancer research projects.