Thermal ablation alone vs thermal ablation combined with transarterial chemoembolization for patients with small (<3 cm) hepatocellular carcinoma.

PURPOSE: Thermal ablation (TA) and transarterial chemoembolization (TACE) may be used alone or in combination (TACE+TA) for the treatment of hepatocellular carcinoma (HCC). The aim of our study was to compare the time to tumor progression (TTP) and overall survival (OS) for patients who received TA alone or TACE+TA for HCC tumors under 3 cm. MATERIALS AND METHODS: This HIPAA-compliant IRB-approved retrospective analysis included 85 therapy-naïve patients from 2010 to 2018 (63 males, 22 females, mean age 62.4 ± 8.5 years) who underwent either TA alone (n = 64) or TA in combination with drug-eluting beads (DEB)-TACE (n = 18) or Lipiodol-TACE (n = 3) for locoregional therapy of early stage HCC with maximum tumor diameter under 3 cm. Kaplan-Meier analysis was performed using the log-rank test to assess TTP and OS. RESULTS: All TA and TACE+TA treatments included were technically successful. TTP was 23.0 months in the TA group and 22.0 months in the TACE+TA group. There was no statistically significant difference in TTP (p = 0.64). Median OS was 69.7 months in the TA group and 64.6 months in the TACE+TA group. There was no statistically significant difference in OS (p = 0.14). The treatment cohorts had differences in AFP levels (p = 0.03) and BCLC stage (p = 0.047). Complication rates between patient groups were similar (p = 0.61). CONCLUSION: For patients with HCC under 3 cm, TA alone and TACE+TA have similar outcomes in terms of TTP and OS, suggesting that TACE+TA may not be needed for these tumors unless warranted by tumor location or other technical consideration.

Prediction of post-radiotherapy locoregional progression in HPV-associated oropharyngeal squamous cell carcinoma using machine-learning analysis of baseline PET/CT radiomics.

Locoregional failure remains a therapeutic challenge in oropharyngeal squamous cell carcinoma (OPSCC). We aimed to devise novel objective imaging biomarkers for prediction of locoregional progression in HPV-associated OPSCC. Following manual lesion delineation, 1037 PET and 1037 CT radiomic features were extracted from each primary tumor and metastatic cervical lymph node on baseline PET/CT scans. Applying random forest machine-learning algorithms, we generated radiomic models for censoring-aware locoregional progression prognostication (evaluated by Harrell's C-index) and risk stratification (evaluated in Kaplan-Meier analysis). A total of 190 patients were included; an optimized model yielded a median (interquartile range) C-index of 0.76 (0.66-0.81; p = 0.01) in prognostication of locoregional progression, using combined PET/CT radiomic features from primary tumors. Radiomics-based risk stratification reliably identified patients at risk for locoregional progression within 2-, 3-, 4-, and 5-year follow-up intervals, with log-rank p-values of p = 0.003, p = 0.001, p = 0.02, p = 0.006 in Kaplan-Meier analysis, respectively. Our results suggest PET/CT radiomic biomarkers can predict post-radiotherapy locoregional progression in HPV-associated OPSCC. Pending validation in large, independent cohorts, such objective biomarkers may improve patient selection for treatment de-intensification trials in this prognostically favorable OPSCC entity, and eventually facilitate personalized therapy.

Reliable prediction of survival in advanced-stage hepatocellular carcinoma treated with sorafenib: comparing 1D and 3D quantitative tumor response criteria on MRI.

OBJECTIVES: To compare 1D and 3D quantitative tumor response criteria applied to DCE-MRI in patients with advanced-stage HCC undergoing sorafenib therapy to predict overall survival (OS) early during treatment. METHODS: This retrospective analysis included 29 patients with advanced-stage HCC who received sorafenib for at least 60 days. All patients underwent baseline and follow-up DCE-MRI at 81.5 ± 29.3 days (range 35-140 days). Response to sorafenib was assessed in 46 target lesions using 1D criteria RECIST1.1 and mRECIST. In addition, a segmentation-based 3D quantification of absolute enhancing lesion volume (vqEASL) was performed on the arterial phase MRI, and the enhancement fraction of total tumor volume (%qEASL) was calculated. Accordingly, patients were stratified into groups of disease control (DC) and disease progression (DP). OS was evaluated using Kaplan-Meier curves with log-rank test and Cox proportional hazards regression model. RESULTS: The Kaplan-Meier analysis revealed that stratification of patients in DC vs. DP according to mRECIST (p = 0.0371) and vqEASL (p = 0.0118) successfully captured response and stratified OS, while stratification according to RECIST and %qEASL did not correlate with OS (p = 0.6273 and p = 0.7474, respectively). Multivariable Cox regression identified tumor progression according to mRECIST and qEASL as independent risk factors of decreased OS (p = 0.039 and p = 0.006, respectively). CONCLUSIONS: The study identified enhancement-based vqEASL and mRECIST as reliable predictors of patient survival early after initiation of treatment with sorafenib. This data provides evidence for potential advantages 3D quantitative, enhancement-based tumor response analysis over conventional techniques regarding early identification of treatment success or failure. KEY POINTS: • Tumor response criteria on MRI can be used to predict survival benefit of sorafenib therapy in patients with advanced HCC. • Stratification into DC and DP using mRECIST and vqEASL significantly correlates with OS (p = 0.0371 and p = 0.0118, respectively) early after initiation of sorafenib, while stratification according to RECIST and %qEASL did not correlate with OS (p = 0.6273 and p = 0.7474, respectively). • mRECIST (HR = 0.325, p = 0.039. 95%CI 0.112-0.946) and qEASL (HR = 0.183, p = 0.006, 95%CI 0.055-0.613) are independent prognostic factors of survival in HCC patients undergoing sorafenib therapy.

Supervised Machine Learning in Oncology: A Clinician's Guide.

The widespread adoption of electronic health records has resulted in an abundance of imaging and clinical information. New data-processing technologies have the potential to revolutionize the practice of medicine by deriving clinically meaningful insights from large-volume data. Among those techniques is supervised machine learning, the study of computer algorithms that use self-improving models that learn from labeled data to solve problems. One clinical area of application for supervised machine learning is within oncology, where machine learning has been used for cancer diagnosis, staging, and prognostication. This review describes a framework to aid clinicians in understanding and critically evaluating studies applying supervised machine learning methods. Additionally, we describe current studies applying supervised machine learning techniques to the diagnosis, prognostication, and treatment of cancer, with a focus on gastroenterological cancers and other related pathologies.