An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Group

In this manuscript we analyze a data set containing information on children with Hodgkin Lymphoma (HL) enrolled on a clinical trial. Treatments received and survival status were collected together with other covariates such as demographics and clinical measurements. Our main task is to explore the potential of machine learning (ML) algorithms in a survival analysis context in order to improve over the Cox Proportional Hazard (CoxPH) model. We discuss the weaknesses of the CoxPH model we would like to improve upon and then we introduce multiple algorithms, from well-established ones to state-of-the-art models, that solve these issues. We then compare every model according to the concordance index and the brier score. Finally, we produce a series of recommendations, based on our experience, for practitioners that would like to benefit from the recent advances in artificial intelligence

Refinement of risk stratification for childhood rhabdomyosarcoma using FOXO1 fusion status in addition to established clinical outcome predictors: A report from the Children's Oncology Group

Background: Previous studies of the prognostic importance of FOXO1 fusion status in patients with rhabdomyosarcoma (RMS) have had conflicting results. We re�examined risk stratification by adding FOXO1 status to traditional clinical prognostic factors in children with localized or metastatic RMS. Methods: Data from six COG clinical trials (D9602, D9802, D9803, ARST0331, ARTS0431, ARST0531; two studies each for low�, intermediate� and high�risk patients) accruing previously untreated patients with RMS from 1997 to 2013 yielded 1727 evaluable patients. Survival tree regression for event�free survival (EFS) was conducted to recursively select prognostic factors for branching and split. Factors included were age, FOXO1, clinical group, histology, nodal status, number of metastatic sites, primary site, sex, tumor size, and presence of metastases in bone/bone marrow, soft tissue, effusions, lung, distant lymph nodes, and other sites. Definition and outcome of the proposed risk groups were compared to existing systems and cross�validated results. Results: The 5�year EFS and overall survival (OS) for evaluable patients were 69% and 79%, respectively. Extent of disease (localized versus metastatic) was the first split (EFS 73% vs 30%; OS 84% vs. 42%). FOXO1 status (positive vs negative) was significant in the second split both for localized (EFS 52% vs 78%; OS 65% vs 88%) and metastatic disease (EFS 6% vs 46%; OS 19% vs 58%). Conclusions: After metastatic status, FOXO1 status is the most important prognostic factor in patients with RMS and improves risk stratification of patients with localized RMS. Our findings support incorporation of FOXO1 status in risk stratified clinical trials

Alveolar Rhabdomyosarcoma with Regional Nodal Involvement: Results of a Combined Analysis from Two Cooperative Groups

BACKGROUND: Treatment of children and adolescents with alveolar rhabdomyosarcoma (ARMS) and regional nodal involvement (N1) have been approached differently by North American and European cooperative groups. In order to define the better therapeutic strategy, we analyzed two studies conducted between 2005 and 2016 by the European paediatric Soft tissue sarcoma Study Group (EpSSG) and Children’s Oncology Group (COG). METHODS: We retrospectively identified patients with ARMS-N1 enrolled in either EpSSG RMS2005 or in COG ARST0531. Chemotherapy in RMS2005 comprised IVADo (ifosfamide, vincristine, dactinomycin, doxorubicin), IVA and maintenance (vinorelbine, cyclophosphamide); in ARST0531 it consisted on either VAC (vincristine, dactinomycin, cyclophosphamide) or VAC alternating with VI (vincristine, irinotecan). Local treatment was similar in both protocols. RESULTS: The analysis of the clinical characteristics of 239 patients showed some differences between study groups: in RMS2005, advanced IRS Group and large tumors predominated. There were no differences in outcomes between the two groups: 5-year event-free survival (EFS), 49%(95%CI=39–59) and 44%(95%CI=30–58), and overall survival (OS), 51%(95%CI=41–61) and 53.6%(95%CI=40–68), in RMS2005 and ARST0531, respectively. In RMS2005, EFS of patients with FOXO1-positive tumors was significantly inferior to those FOXO1-negative (49.3% vs 73%, p=0.034). In contrast, in ARST0531, EFS of patients with FOXO1-positive tumors was 45% compared with 43.8% for those FOXO1-negative. CONCLUSIONS: The outcome of patients with ARMS N1 was similar in both protocols. However, patients with FOXO1 fusion-negative tumors enrolled in RMS2005 showed a significantly better outcome, suggesting that different strategies of chemotherapy may have an impact in the outcome of this subgroup of patients

Automated Breast Density Measurements From Chest Computed Tomography Scans.

To develop an automated method for quantifying percent breast density from chest computed tomography (CT) scans. A naïve Bayesian classifier based on gray-level intensities and spatial relationships was developed on CT scans from 10 patients diagnosed with Hodgkin lymphoma (HL) and imaged as part of routine clinical care. The algorithm was validated on CT scans from 75 additional HL patients. The classifier was developed and validated using a reference dataset with consensus manual segmentation of fibroglandular tissue. Accuracy was evaluated at the pixel-level to examine how well the algorithm identified pixels with fibroglandular tissue using true and false positive fractions (TPF and FPF, respectively). Quantitative estimates of the patient-level CT percent density were contrasted to each other using the concordance correlation coefficient, ρc, and to subjective ACR BI-RADS density assessments using Kendalls τb. The pixel-level TPF for identifying pixels with fibroglandular tissue was 82.7% (interquartile range of patient-specific TPFs 65.5%-89.6%). The pixel-level FPF was 9.2% (interquartile range of patient-specific FPFs 2.5%-45.3%). Patient-level agreement of the algorithms automated density estimate with that obtained from the reference dataset was high, ρc = 0.93 (95% CI 0.90-0.96) as was agreement with a radiologists subjective ACR-BI-RADS assessments, τb = 0.77. It is possible to obtain automated measurements of percent density from clinical CT scans