Margriet Kwint

Acute esophagus toxicity in lung cancer patients after intensity modulated radiation therapy and concurrent chemotherapy.

PURPOSE The purpose of this study was to investigate the dose-effect relation between acute esophageal toxicity (AET) and the dose-volume parameters of the esophagus after intensity modulated radiation therapy (IMRT) and concurrent chemotherapy for patients with non-small cell lung cancer (NSCLC). PATIENTS AND METHODS One hundred thirty-nine patients with inoperable NSCLC treated with IMRT and concurrent chemotherapy were prospectively analyzed. The fractionation scheme was 66 Gy in 24 fractions. All patients received concurrently a daily dose of cisplatin (6 mg/m(2)). Maximum AET was scored according to Common Toxicity Criteria 3.0. Dose-volume parameters V5 to V70, D(mean) and D(max) of the esophagus were calculated. A logistic regression analysis was performed to analyze the dose-effect relation between these parameters and grade ≥ 2 and grade ≥ 3 AET. The outcome was compared with the clinically used esophagus V35 prediction model for grade ≥ 2 after radical 3-dimensional conformal radiation therapy (3DCRT) treatment. RESULTS In our patient group, 9% did not experience AET, and 31% experienced grade 1 AET, 38% grade 2 AET, and 22% grade 3 AET. The incidence of grade 2 and grade 3 AET was not different from that in patients treated with CCRT using 3DCRT. The V50 turned out to be the most significant dosimetric predictor for grade ≥ 3 AET (P=.012). The derived V50 model was shown to predict grade ≥ 2 AET significantly better than the clinical V35 model (P<.001). CONCLUSIONS For NSCLC patients treated with IMRT and concurrent chemotherapy, the V50 was identified as most accurate predictor of grade ≥ 3 AET. There was no difference in the incidence of grade ≥ 2 AET between 3DCRT and IMRT in patients treated with concurrent chemoradiation therapy.

Relating acute esophagitis to radiotherapy dose using FDG-PET in concurrent chemo-radiotherapy for locally advanced non-small cell lung cancer

PURPOSE To correlate radiotherapy (RT) dose to acute esophagitis (AE) by means of FDG-PET scans acquired after concurrent chemo-radiotherapy (cCRT) for locally advanced non-small-cell lung cancer (NSCLC). MATERIALS AND METHODS Patients treated with 24 × 2.75 Gy were selected on presence of a post-RT PET (PET(post)) scan acquired within 3 months after cCRT. The value of PET(post) in relation to AE was evaluated by comparing the mean esophageal SUV of the highest 50% (mathematical left angle bracket SUV(50%) mathematical right angle bracket) between gr < 2 and gr ≥ 2AE. The local dose on the esophagus wall was correlated to the SUV and modeled using a power-law fit. The Lyman-Kutcher-Burman (LKB) model was used to predict gr ≥ 2AE. The local dose-response relation was used in the LKB model to calculate the EUD. Resulting prediction accuracy was compared to D(mean), V(35), V(55) and V(60). RESULTS Eighty-two patients were included (gr < 2 = 25, gr ≥ 2=57). The mathematical left angle bracket SUV(50%) mathematical right angle bracket ≥ was significantly higher for gr ≥ 2AE (2.2 vs. 2.6, p < 0.01). The LKB parameters (95% CI) were n = 0.130 (0.120-0.141), m = 0.25 (0.13-0.85) and TD(50) = 50.4 Gy (37.5-55.4), which resulted in improved predictability of AE compared to other predictors. CONCLUSION Esophageal uptake of FDG post-cCRT reflects AE severity. Predictability of grade ≥ 2AE was improved by using the local dose-SUV response model, with narrow confidence intervals for the optimized LKB parameters.

Prognostic parameters for acute esophagus toxicity in Intensity Modulated Radiotherapy and concurrent chemotherapy for locally advanced non-small cell lung cancer

BACKGROUND AND PURPOSE The aim of this study was to correlate clinical and dosimetric variables with acute esophageal toxicity (AET) following Intensity Modulated Radiotherapy (IMRT) with concurrent chemotherapy for locally advanced non-small cell lung cancer (NSCLC). In addition, timeline of AET was reported. MATERIAL AND METHODS 153 patients with locally advanced NSCLC treated with 66 Gy/2.75 Gy/24 fractions of radiotherapy and concurrent daily low dose cisplatin were selected. Medical records and treatments of these patients were retrospectively reviewed. Maximum AET grade ≥2 and maximum grade 3 were the endpoints of this study. Dates for onset, maximum and recovery (to baseline) of AET were reported. Univariate and multivariate analysis were applied to correlate clinical, tumor, dosimetric and chemotherapy dose variables to AET grade ≥2 and grade 3. RESULTS AET grade 2 occurred in 37% and grade 3 in 20% of the patients. The median onset of AET was around day 15 for all grades. The median onset of the maximum grade was day 30 for both grades 2 and 3. The median duration was 43 days for grade 1, 50 days for grade 2 and >80 days for grade 3. Of the grade 3 AET patients, 48% recovered within 3 months. Esophagus V50, ethnic background, and the number of cisplatin administrations were significantly correlated with grade 3 AET. CONCLUSIONS For NSCLC patients treated with concurrent chemotherapy and IMRT A higher number of cisplatin administrations, non-Caucasian background and higher V50oes were associated with grade 3 AET. The median onset of AET grade 3 is 15 days after the start of treatment, maximized at day 30, with a median duration of >80 days.

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

Purpose Machine 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. Methods We 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, Cohens 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). Results Random 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. Conclusion Random 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 ones own modeling results against. Our results also show that an informed preselection of classifiers based on existing datasets can improve discrimination over random selection.

Outcome of radical local treatment of non-small cell lung cancer patients with synchronous oligometastases

OBJECTIVES Patients with stage IV non-small cell lung cancer (NSCLC) are considered incurable and are mainly treated with palliative intent. This patient group has a poor overall survival (OS) and progression free survival (PFS). The purpose of this study was to investigate PFS and OS of NSCLC patients diagnosed with synchronous oligometastatic disease who underwent radical treatment of both intrathoracic disease and metastases. MATERIALS AND METHODS Patients with NSCLC and oligometastatic disease at diagnosis, who were treated with radical intent between 2008 and 2016, were included in this observational study. Treatment consisted of systemic treatment and radical radiotherapy or resection of the intrathoracic disease. Treatment of the metastases consisted of radical or stereotactic radiotherapy, surgical resection or radiofrequency ablation. RESULTS AND CONCLUSIONS Ninety-one patients (52% men, mean age 60 years) in good performance status were included. Thirty-eight patients (42%) died during follow-up (median follow-up 35 months). The cause of dead was lung cancer in all patients, except one. Sixty-three (69%) patients developed recurrent disease. Eleven recurrences (17%) occurred within the irradiated area. For the whole group, the median PFS was 14 months (range 2-89, 95%CI 12-16) and the median OS was 32 months (range 3-89, 95%CI 25-39). The 1- and 2-year OS rates were 85% and 58% and the 1- and 2-year PFS rates were 55% and 27%, respectively. Radical local treatment of a selected group of NSCLC patients with good performance status presenting with synchronous oligometastatic disease resulted in favorable long-term PFS and OS.

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.

Spontaneous Regression of Large Cell Carcinoma of the Lung, a Case Report

Spontaneous regression of malignancies are a rare biological phenomenon, especially in lung cancer. Here, we present a case of an 80-year old man, with a cT2aN3M1b non-small cell lung cancer of the left upper lobe, whose tumor regressed spontaneously without any treatment. The cause of the spontaneous tumor remission remains unknown in this case. We describe this rare case and review the literature pertaining to spontaneous regression of lung cancer.