Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, findingthe optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-anderror process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows perapplication. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms foreach component. To optimize the workflow per application, we employ automated machine learning using a random search andensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1)liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77);5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis(0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer’s disease (0.87); and 12) head and neck cancer (0.84). Weshow that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performssimilar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automaticallyoptimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications.To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework,and the code to reproduce this study

Transarterial Chemoembolization With Drug-Eluting Beads Versus Stereotactic Body Radiation Therapy for Hepatocellular Carcinoma: Outcomes From a Multicenter, Randomized, Phase 2 Trial (the TRENDY Trial)

Purpose: To compare transarterial chemoembolization delivered with drug eluting beads (TACE-DEB) with stereotactioc body radiation therapy (SBRT) in patients with hepatocellular carcinoma (HCC) in a multicenter randomized trial. Methods and Materials: Patients were included if they were eligible for TACE. They could also be recruited if they required treatment prior to liver transplantation. A maximum of four TACE-DEB procedures and ablation after incomplete TACE-DEB were both allowed. SBRT was delivered in six fractions of 8-9Gy. Primary end point was time to progression (TTP). Secondary endpoints were local control (LC), overall survival (OS), response rate (RR), toxicity, and quality of life (QoL). The calculated sample size was 100 patients. Results: Between May 2015 and April 2020, 30 patients were randomized to the study. Due to slow accrual the trial was closed prematurely. Two patients in the SBRT arm were considered ineligible leaving 16 patients in the TACE-DEB arm and 12 in the SBRT arm. Median follow-up was 28.1 months. Median TTP was 12 months for TACEDEB and 19 months for SBRT (p=0.15). Median LC was 12 months for TACE-DEB and >40 months (not reached) for SBRT (p=0.075). Median OS was 36.8 months for TACEDEB and 44.1 months for SBRT (p=0.36). A post-hoc analysis showed 100% for SBRT 1- and 2-year LC, and 54.4% and 43.6% for TACE-DEB (p=0.019). Both treatments resulted in RR>80%. Three episodes of possibly related toxicity grade ≥3 were observed after TACE-DEB. No episodes were observed after SBRT. QoL remained stable after both treatment arms. Conclusions: In this trial, TTP after TACE-DEB was not significantly improved by SBRT, while SBRT showed higher local antitumoral activity than TACE-DEB, without detrimental effects on OS, toxicity and QoL. To overcome poor accrual in randomized trials that include SBRT, and to generate evidence for including SBRT in treatment guidelines, international cooperation is needed