Phase 3 Randomized Trial of Prophylactic Cranial Irradiation With or Without Hippocampus Avoidance in SCLC (NCT01780675)

Introduction: To compare neurocognitive functioning in patients with SCLC who received prophylactic cranial irradiation (PCI) with or without hippocampus avoidance (HA). Methods: In a multicenter, randomized phase 3 trial (NCT01780675), patients with SCLC were randomized to standard PCI or HA-PCI of 25 Gy in 10 fractions. Neuropsychological tests were performed at baseline and 4, 8, 12, 18, and 24 months after PCI. The primary end point was total recall on the Hopkins Verbal Learning Test-Revised at 4 months; a decline of at least five points from baseline was considered a failure. Secondary end points included other cognitive outcomes, evaluation of the incidence, location of brain metastases, and overall survival. Results: From April 2013 to March 2018, a total of 168 patients were randomized. The median follow-up time was 26.6 months. In both treatment arms, 70% of the patients had limited disease and baseline characteristics were well balanced. Decline on the Hopkins Verbal Learning Test-Revised total recall score at 4 months was not significantly different between the arms: 29% of patients on PCI and 28% of patients on HA-PCI dropped greater than or equal to five points (p = 1.000). Performance on other cognitive tests measuring memory, executive function, attention, motor function, and processing speed did not change significantly different over time between the groups. The overall survival was not significantly different (p = 0.43). The cumulative incidence of brain metastases at 2 years was 20% (95% confidence interval: 12%-29%) for the PCI arm and 16% (95% confidence interval: 7%-24%) for the HA-PCI arm. Conclusions: This randomized phase 3 trial did not find a lower probability of cognitive decline in patients with SCLC receiving HA-PCI compared with conventional PCI. No increase in brain metastases at 2 years was observed in the HA-PCI arm. (C) 2021 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved

The use of real-world evidence to audit normal tissue complication probability models for acute esophageal toxicity in non-small cell lung cancer patients

Contains fulltext : 220483.pdf (publisher's version ) (Closed access)INTRODUCTION: The aim of this work is to assess the validity of real world data (RWD) derived from an electronic toxicity registration (ETR). As a showcase, the NTCP-models of acute esophageal toxicity (AET) for concurrent chemoradiation (CCRT) for NSCLC patients were used to validate the ETR of AET before/after dose de-escalation to the mediastinal lymph nodes. MATERIAL AND METHODS: One hundred and one patients received 24 × 2.75 Gy and 116 patients received de-escalated dose of 24 × 2.42 Gy to the mediastinal lymph nodes. The validity and completeness of the ETR was analyzed. The grade ≥2 AET probability was defined according the V50 Gy and V60 Gy NTCP-models from literature. Validity of the models was assessed by calibration and discrimination. Furthermore, sensitivity and specificity for different cut-off points were determined. RESULTS: The compliance of ETR was 73-80%, with sensitivity and specificity rates of 83% and 86% for grade ≥2 AET, respectively. Discrimination of both NTCP-models demonstrated a moderate accuracy (V50 model, AUC 0.71; V60-model, AUC 0.69). Dose de-escalation did not influence the accuracy of the V50-model; AUC before: 0.69, and AUC after: 0.71. For the V60-model the model-accuracy decreased after dose de-escalation; AUC before: 0.72 and AUC after: 0.62, respectively. CONCLUSION: RWD is a useful method to audit NTCP models in clinical practice. The NTCP models to predict AET in NSCLC patients showed moderate predictive accuracy. For clinical practice, the V50Gy seems to be most stable for dose de-escalation without compromising safety and efficacy

External validation of an NTCP model for acute esophageal toxicity in locally advanced NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

BACKGROUND AND PURPOSE: We externally validated a previously established multivariable normal-tissue complication probability (NTCP) model for Grade >/=2 acute esophageal toxicity (AET) after intensity-modulated (chemo-)radiotherapy or volumetric-modulated arc therapy for locally advanced non-small cell lung cancer. MATERIALS AND METHODS: A total of 603 patients from five cohorts (A-E) within four different Dutch institutes were included. Using the NTCP model, containing predictors concurrent chemoradiotherapy, mean esophageal dose, gender and clinical tumor stage, the risk of Grade >/=2 AET was estimated per patient and model discrimination and (re)calibration performance were evaluated. RESULTS: Four validation cohorts (A, B, D, E) experienced higher incidence of Grade >/=2 AET compared to the training cohort (49.3-70.2% vs 35.6%; borderline significant for one cohort, highly significant for three cohorts). Cohort C experienced lower Grade >/=2 AET incidence (21.7%, p<0.001). For three cohorts (A-C), discriminative performance was similar to the training cohort (area under the curve (AUC) 0.81-0.89 vs 0.84). In the two remaining cohorts (D-E) the model showed poor discriminative power (AUC 0.64 and 0.63). Reasonable calibration performance was observed in two cohorts (A-B), and recalibration further improved performance in all three cohorts with good discrimination (A-C). Recalibration for the two poorly discriminating cohorts (D-E) did not improve performance. CONCLUSIONS: The NTCP model for AET prediction was successfully validated in three out of five patient cohorts (AUC >/=0.80). The model did not perform well in two cohorts, which included patients receiving substantially different treatment. Before applying the model in clinical practice, validation of discrimination and (re)calibration performance in a local cohort is recommended

Phase 3 Randomized Trial of Prophylactic Cranial Irradiation With or Without Hippocampus Avoidance in SCLC (NCT01780675)

Introduction: To compare neurocognitive functioning in patients with SCLC who received prophylactic cranial irradiation (PCI) with or without hippocampus avoidance (HA). Methods: In a multicenter, randomized phase 3 trial (NCT01780675), patients with SCLC were randomized to standard PCI or HA-PCI of 25 Gy in 10 fractions. Neuropsychological tests were performed at baseline and 4, 8, 12, 18, and 24 months after PCI. The primary end point was total recall on the Hopkins Verbal Learning Test—Revised at 4 months; a decline of at least five points from baseline was considered a failure. Secondary end points included other cognitive outcomes, evaluation of the incidence, location of brain metastases, and overall survival. Results: From April 2013 to March 2018, a total of 168 patients were randomized. The median follow-up time was 26.6 months. In both treatment arms, 70% of the patients had limited disease and baseline characteristics were well balanced. Decline on the Hopkins Verbal Learning Test-Revised total recall score at 4 months was not significantly different between the arms: 29% of patients on PCI and 28% of patients on HA-PCI dropped greater than or equal to five points (p = 1.000). Performance on other cognitive tests measuring memory, executive function, attention, motor function, and processing speed did not change significantly different over time between the groups. The overall survival was not significantly different (p = 0.43). The cumulative incidence of brain metastases at 2 years was 20% (95% confidence interval: 12%–29%) for the PCI arm and 16% (95% confidence interval: 7%–24%) for the HA-PCI arm. Conclusions: This randomized phase 3 trial did not find a lower probability of cognitive decline in patients with SCLC receiving HA-PCI compared with conventional PCI. No increase in brain metastases at 2 years was observed in the HA-PCI arm

Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers.

PurposeMachine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. The purpose of this study is to compare such classifiers specifically for (chemo)radiotherapy datasets and to estimate their average discriminative performance for radiation treatment outcome prediction.MethodsWe collected 12 datasets (3496 patients) from prior studies on post-(chemo)radiotherapy toxicity, survival, or tumor control with clinical, dosimetric, or blood biomarker features from multiple institutions and for different tumor sites, that is, (non-)small-cell lung cancer, head and neck cancer, and meningioma. Six common classification algorithms with built-in feature selection (decision tree, random forest, neural network, support vector machine, elastic net logistic regression, LogitBoost) were applied on each dataset using the popular open-source R package caret. The R code and documentation for the analysis are available online (https://github.com/timodeist/classifier_selection_code). All classifiers were run on each dataset in a 100-repeated nested fivefold cross-validation with hyperparameter tuning. Performance metrics (AUC, calibration slope and intercept, accuracy, Cohen's kappa, and Brier score) were computed. We ranked classifiers by AUC to determine which classifier is likely to also perform well in future studies. We simulated the benefit for potential investigators to select a certain classifier for a new dataset based on our study (pre-selection based on other datasets) or estimating the best classifier for a dataset (set-specific selection based on information from the new dataset) compared with uninformed classifier selection (random selection).ResultsRandom forest (best in 6/12 datasets) and elastic net logistic regression (best in 4/12 datasets) showed the overall best discrimination, but there was no single best classifier across datasets. Both classifiers had a median AUC rank of 2. Preselection and set-specific selection yielded a significant average AUC improvement of 0.02 and 0.02 over random selection with an average AUC rank improvement of 0.42 and 0.66, respectively.ConclusionRandom forest and elastic net logistic regression yield higher discriminative performance in (chemo)radiotherapy outcome and toxicity prediction than other studied classifiers. Thus, one of these two classifiers should be the first choice for investigators when building classification models or to benchmark one's own modeling results against. Our results also show that an informed preselection of classifiers based on existing datasets can improve discrimination over random selection

Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145237/1/mp12967_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145237/2/mp12967.pd