Managing RT Schedules of Early-Stage Breast Cancer Patients with a Genetic-Dosimetric Validated Model for Late Fibrosis

PURPOSE/OBJECTIVE(S)

Define a multifactorial risk prediction model for RT-induced fibrosis and investigate the benefit of a personalized approach for breast cancer (BC) patients (pts) treated with whole breast RT.

MATERIALS/METHODS

In a previous study, we confirmed the predictive role of 30 SNPs from the literature and built an interaction aware Polygenic Risk Score (PRS, following the methods from Franco RO 2021) for Late Fibrosis (FG2+) on a cohort of 1500 pts from the REQUITE EU/USA prospective observational study. The PRS weights the radiosensitive (RS) and radioresistant (RR) genetic components and can be included in NTCP models. In a subgroup from the same cohort (390 pts), we have also confirmed an NTCP model based on biologically Equivalent Uniform Dose (BEUD) from PTV DVHs for pts treated at 40-50 Gy and no RT boost. Here, we combine PRS and BEUD into a sigmoid model allowing PRS to modulate BEUD50 (BEUD leading to 50% FG2+), i.e., we permitted a personalized BEUD50. We can also consider this as translating the PRS into a personalized equivalent BEUD, which is added/subtracted to the treatment BEUD. We evaluated model performances through ROC-AUC, calibration plot and Precision-Recall AUC.

RESULTS

A total of 381 pts had complete dosimetric/genetic data, prescribed dose 40-50 Gy, and no fibrotic alteration at RT start. We scored FG2+ in 87 pts (23%). PRS ranged from -13 (more RR pts) to 7 (more RS), and a unit in PRS corresponds to 5.3 Gy BEUD or 3 Gy in EQ EUD2 Gy. Table 1 summarizes model performances, with details for subgroups below/above the quartiles I/III of the BEUD distribution. The PRS-only model correctly describes the toxicity rates in the whole population (calibration slope/offset = 0/1). Still, it overestimates/underestimates the absolute risks in the low/high dose ranges. The integrated model improves AUC-ROC and AUC-PRC by 5% and 10% and guarantees a better calibration in pts receiving low/high BEUD to the PTV.

CONCLUSION

We developed a multifactorial model for FG2+ based on two previously validated models and reported the improvement against single-factor models. The BEUD+PRS model is suitable for assisting clinicians in managing early-stage BC pts. The number of fractions or the daily dose could be reduced for RS pts. The integrated model resulted in a possible quantitative tool for driving the planning decision process. Also, it showed a better performance in the high BEUD region, suggesting the potential value of its extension toward RT including boost or ultra hypofractionation. We are testing this extension in the whole REQUITE cohort.

Distant supervision for imaging-based cancer sub-typing in Intrahepatic Cholangiocarcinoma

Finding effective ways to perform cancer sub-typing is currently a trending research topic for therapy opti-mization and personalized medicine. Stemming from genomic field, several algorithms have been proposed. In the context of texture analysis, limited efforts have been attempted, yet imaging information is known to entail useful knowledge for clinical practice. We propose a distant supervision model for imaging-based cancer sub-typing in Intrahepatic Cholangiocar-cinoma patients. A clinically informed stratification of patients is built and homogeneous groups of patients are characterized in terms of survival probabilities, qualitative cancer variables and radiomic feature description. Moreover, the contributions of the information derived from the ICC area and from the peri tumoral area are evaluated. The findings suggest the reliability of the proposed model in the context of cancer research and testify the importance of accounting for data coming from both the tumour and the tumour-tissue interface. Clinical relevance - In order to accurately predict cancer prognosis for patients affected by ICC, radiomic variables of both core cancer and surrounding area should be exploited and employed in a model able to manage complex information.

[18F]FDG-PET/CT radiomics in patients suspected of infective endocarditis

Funding Acknowledgements

Type of funding sources: None.

AIM [18F]FDG-PET/CT is part of the diagnostic algorithm for IE diagnosis. Increased [18F]FDG uptake with focal and heterogenous pattern at valve, intravalvular or perivalvular at visual analysis is consistent with IE. Diffuse, homogeneous or low valvular [18F]FDG uptake make diagnosis more challenging. Semiquantitative parameters may be of value in such case of equivocal PET findings; however, they are still not validated in IE. In this study we aim to assess the value of [18F]FDG PET/CT radiomics in IE diagnosis. Further, we build a model for radiomics-based prediction of PET/CT findings, patient classification and stratification as well as prediction of the final diagnosis. Materials and Methods We evaluated a series of [18F]FDG PET/CT scans in 447 patients (M:F =284:163, mean age 67± 16yrs), with suspected IE (519 valves, NVE = 109, PVE = 410), studied in 3 different centers between January 2015- 2020. Clinical, surgical data, antimicrobial treatment, microbiology and biochemistry, imaging and the DUKE/2015 ESC classification were collected. PET/CT images were semiautomatically segmented (Advantage Workstation, GE) and texture features extracted by LIFEx software. For the analysis we used absolute correlation exclusion criteria and PCA based dimensionality reduction, MANOVA test and LR for multivariate testing. Prior to model building by Random Forest (80% training sets, 20% test), we applied covariance matrix for correlated feature removal and SMOTE for preprocessing the imbalanced dataset. Results MANOVA and LR showed a positive contribution of radiomics in predicting PET/CT results and IE diagnosis, with a different signature in IE-positive/IE-negative patients (80% in training, 70% in validation). Of interest, the signature of patients with equivocal PET/CT findings was similar to IE-negative signature. Clustering-based stratification identify in two groups, one with milder disease presenting weak or no [18F]FDG uptake and one with more severe disease. Our LR models with incremental complexity (Table 1 and 2) demonstrated that the richer the information fed into the model the higher the performances, reaching 90% of AUC. However, the performance of model M5 and M6 is almost equal, suggesting a limited contribution of radiomics in classifying IE. Conclusion [18F]FDG PET/CT radiomics provide a limited, yet positive, contribution in the classification of EI. Nevertheless, radiomics was fundamental in defining PET outcome, thus it could support visual imaging assessment in particular when equivocal [18F]FDG findings are present. Further steps focusing on refinement of the IE diagnostic criteria, on explainable analysis on positive/negative patients to be transferred in equivocal cases. Ultimately, the identification of radiomic signature would help to define thresholds to discriminate between mild infection and severe IE, in a risk score fashion. Abstract Table 1  Abstract Table 2

Recurrence-specific supervised graph clustering for subtyping Hodgkin Lymphoma radiomic phenotypes

The prediction at baseline of patients at high risk for therapy failure or recurrence would significantly impact on Hodgkin Lymphoma patients treatment, informing clinical practice. Current literature is extensively searching insights in radiomics, a promising framework for high-throughput imaging feature extraction, to derive biomarkers and quantitative prognostic factors from images. However, existing studies are limited by intrinsic radiomic limitations, high dimensionality among others. We propose an exhaustive patient representation and a recurrence-specific multi-view supervised clustering algorithm for estimating patient-to-patient similarity graph and learning recurrence probability. We stratified patients in two risk classes and characterize each group in terms of clinical variables.