Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy.

Background and purposeChest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose-volume constraints.Materials and methodsTwenty-five patient, tumor, and dosimetric features were recorded for 197 consecutive patients with Stage I NSCLC treated with SBRT, 11 of whom (5.6%) developed CTCAEv4 grade ≥2 chest wall pain. Decision tree modeling was used to determine chest wall syndrome (CWS) thresholds for individual features. Significant features were determined using independent multivariate methods. These methods incorporate out-of-bag estimation using Random forests (RF) and bootstrapping (100 iterations) using decision trees.ResultsUnivariate analysis identified rib dose to 1 cc < 4000 cGy (P = 0.01), chest wall dose to 30 cc < 1900 cGy (P = 0.035), rib Dmax < 5100 cGy (P = 0.05) and lung dose to 1000 cc < 70 cGy (P = 0.039) to be statistically significant thresholds for avoiding CWS. Subsequent multivariate analysis confirmed the importance of rib dose to 1 cc, chest wall dose to 30 cc, and rib Dmax. Using learning-curve experiments, the dataset proved to be self-consistent and provides a realistic model for CWS analysis.ConclusionsUsing machine learning algorithms in this first of its kind study, we identify robust features and cutoffs predictive for the rare clinical event of CWS. Additional data in planned subsequent multicenter studies will help increase the accuracy of multivariate analysis

Validation and clinical implementation of an accurate Monte Carlo code for pencil beam scanning proton therapy.

Monte Carlo (MC)-based dose calculations are generally superior to analytical dose calculations (ADC) in modeling the dose distribution for proton pencil beam scanning (PBS) treatments. The purpose of this paper is to present a methodology for commissioning and validating an accurate MC code for PBS utilizing a parameterized source model, including an implementation of a range shifter, that can independently check the ADC in commercial treatment planning system (TPS) and fast Monte Carlo dose calculation in opensource platform (MCsquare). The source model parameters (including beam size, angular divergence and energy spread) and protons per MU were extracted and tuned at the nozzle exit by comparing Tool for Particle Simulation (TOPAS) simulations with a series of commissioning measurements using scintillation screen/CCD camera detector and ionization chambers. The range shifter was simulated as an independent object with geometric and material information. The MC calculation platform was validated through comprehensive measurements of single spots, field size factors (FSF) and three-dimensional dose distributions of spread-out Bragg peaks (SOBPs), both without and with the range shifter. Differences in field size factors and absolute output at various depths of SOBPs between measurement and simulation were within 2.2%, with and without a range shifter, indicating an accurate source model. TOPAS was also validated against anthropomorphic lung phantom measurements. Comparison of dose distributions and DVHs for representative liver and lung cases between independent MC and analytical dose calculations from a commercial TPS further highlights the limitations of the ADC in situations of highly heterogeneous geometries. The fast MC platform has been implemented within our clinical practice to provide additional independent dose validation/QA of the commercial ADC for patient plans. Using the independent MC, we can more efficiently commission ADC by reducing the amount of measured data required for low dose "halo" modeling, especially when a range shifter is employed

Assessing and testing anomaly detection for finding prostate cancer in spatially registered multi-parametric MRI

BackgroundEvaluating and displaying prostate cancer through non-invasive imagery such as Multi-Parametric MRI (MP-MRI) bolsters management of patients. Recent research quantitatively applied supervised target algorithms using vectoral tumor signatures to spatially registered T1, T2, Diffusion, and Dynamic Contrast Enhancement images. This is the first study to apply the Reed-Xiaoli (RX) multi-spectral anomaly detector (unsupervised target detector) to prostate cancer, which searches for voxels that depart from the background normal tissue, and detects aberrant voxels, presumably tumors.MethodsMP-MRI (T1, T2, diffusion, dynamic contrast-enhanced images, or seven components) were prospectively collected from 26 patients and then resized, translated, and stitched to form spatially registered multi-parametric cubes. The covariance matrix (CM) and mean μ were computed from background normal tissue. For RX, noise was reduced for the CM by filtering out principal components (PC), regularization, and elliptical envelope minimization. The RX images were compared to images derived from the threshold Adaptive Cosine Estimator (ACE) and quantitative color analysis. Receiver Operator Characteristic (ROC) curves were used for RX and reference images. To quantitatively assess algorithm performance, the Area Under the Curve (AUC) and the Youden Index (YI) points for the ROC curves were computed.ResultsThe patient average for the AUC and [YI] from ROC curves for RX from filtering 3 and 4 PC was 0.734[0.706] and 0.727[0.703], respectively, relative to the ACE images. The AUC[YI] for RX from modified Regularization was 0.638[0.639], Regularization 0.716[0.690], elliptical envelope minimization 0.544[0.597], and unprocessed CM 0.581[0.608] using the ACE images as Reference Image. The AUC[YI] for RX from filtering 3 and 4 PC was 0.742[0.711] and 0.740[0.708], respectively, relative to the quantitative color images. The AUC[YI] for RX from modified Regularization was 0.643[0.648], Regularization 0.722[0.695], elliptical envelope minimization 0.508[0.605], and unprocessed CM 0.569[0.615] using the color images as Reference Image. All standard errors were less than 0.020.ConclusionsThis first study of spatially registered MP-MRI applied anomaly detection using RX, an unsupervised target detection algorithm for prostate cancer. For RX, filtering out PC and applying Regularization achieved higher AUC and YI using ACE and color images as references than unprocessed CM, modified Regularization, and elliptical envelope minimization

Building more accurate decision trees with the additive tree.

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches

Chemoradiotherapy versus chemotherapy alone for unresected intrahepatic cholangiocarcinoma: practice patterns and outcomes from the national cancer data base

Background: Current guidelines recommend chemotherapy (CT) with or without radiotherapy (RT) for unresected intrahepatic cholangiocarcinoma (IC). Although there is currently lack of consensus, previous smaller studies have illustrated the efficacy of local therapy for this population. This investigation evaluated outcomes of chemoradiotherapy (CRT) versus CT alone in unresected IC using a large, contemporary national database. Methods: The National Cancer Data Base (NCDB) was queried for primary IC cases (2004-2013) receiving CT alone or CRT. Patients undergoing resection or not receiving CT were excluded, as were those with M1 disease or unknown M classification. Logistic regression analysis ascertained factors associated with CRT administration. Kaplan-Meier analysis evaluated overall survival (OS) between both groups. Cox proportional hazards modeling assessed variables associated with OS. Results: In total, 2,842 patients were analyzed [n=666 (23%) CRT, n=2,176 (77%) CT]. CRT was less likely delivered at community centers, in more recent time periods (2009-2013), to older patients, and in certain geographic locations. Median OS in the CRT and CT groups were 13.6 vs. 10.5 months, respectively (P<0.001). On multivariate analysis, poorer OS was associated with age, male gender, increased comorbidities, treatment at a community center, and treatment at earlier time periods (2004-2008) (P<0.05 for all). Notably, receipt of CRT independently predicted for improved OS (P<0.001). Conclusions: As compared to CT alone, CRT was independently associated with improved survival in unresected IC. These findings support a randomized trial evaluating this question that is currently accruing

Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications

The radioprotectant nano-genistein enhances radiotherapy efficacy of lung tumors in mice

BACKGROUND: Radiotherapy for non-small cell lung cancer (NSCLC) can be dose-limiting due to treatment-related toxicities. Genistein has been shown to be a robust radioprotective agent in preclinical models. A novel genistein oral nanosuspension formulation (nano-genistein) has demonstrated efficacy in mitigating radiation-induced lung damage in preclinical animal models. However, while those studies have confirmed that nano-genistein can protect normal lung tissue from radiation-induced toxicities, no studies have assessed the effect of nano-genistein on lung tumors. Here, we evaluated the impact of nano-genistein on the efficacy of radiation treatment of lung tumors in a mouse xenograft model. METHODS: Two separate studies were conducted utilizing human A549 cells implanted either dorsally within the upper torso or in the flank. Daily oral administration of nano-genistein (200 or 400 mg/kg/day) occurred prior to and after exposure to a single dose of thoracic or abdominal 12.5 Gy radiation. Tumor growth was monitored twice weekly, nano-genistein treatment continued for up to 20 weeks and histopathology of tissues was completed post euthanasia. RESULTS: Continuous nano-genistein dosing was safe across all study groups in both studies. Animals receiving nano-genistein better maintained body weight following irradiation compared to corresponding vehicle treated animals. Animals that received nano-genistein also had reduced tumor growth and improved normal lung histopathology compared to those receiving vehicle suggesting that nano-genistein does not protect tumors from radiotherapy but is radioprotective of the lungs. There were no treatment-related histopathological findings noted in the skin adjacent to the tumor, esophagus, or uterus. CONCLUSIONS: These results, including the safety following extended dosing, support the continued evaluation of nano-genistein as an adjunctive treatment for patients with NSCLC undergoing radiotherapy and serve as the basis of a phase 1b/2a multicenter clinical trial