The use of machine learning and other sophisticated models to aid in prediction and decision making has become widely popular across a breadth of disciplines. Within the greater diagnostic radiology, radiation oncology, and medical physics communities promising work is being performed in tissue classification and cancer staging, outcome prediction, automated segmentation, treatment planning, and quality assurance as well as other areas. In this article, machine learning approaches are explored, highlighting specific applications in machine and patient-specific quality assurance (QA). Machine learning can analyze multiple elements of a delivery system on its performance over time including the multileaf collimator (MLC), imaging system, mechanical and dosimetric parameters. Virtual Intensity-Modulated Radiation Therapy (IMRT) QA can predict passing rates using different measurement techniques, different treatment planning systems, and different treatment delivery machines across multiple institutions. Prediction of QA passing rates and other metrics can have profound implications on the current IMRT process. Here we cover general concepts of machine learning in dosimetry and various methods used in virtual IMRT QA, as well as their clinical applications.

Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but there is much work to be done. In this article, we approach the radiotherapy process from a workflow perspective, identifying specific areas where a data-centric approach using ML could improve the quality and efficiency of patient care. We highlight areas where ML has already been used, and identify areas where we should invest additional resources. We believe that this article can serve as a guide for both clinicians and researchers to start discussing issues that must be addressed in a timely manner.

The big data revolution presents an exciting frontier to expand public health research, broadening the scope of research and increasing the precision of answers. Despite these advances, scientists must be vigilant against also advancing potential harms toward marginalized communities. In this review, we provide examples in which big data applications have (unintentionally) perpetuated discriminatory practices, while also highlighting opportunities for big data applications to advance equity in public health. Here, big data is framed in the context of the five Vs (volume, velocity, veracity, variety, and value), and we propose a sixth V, virtuosity, which incorporates equity and justice frameworks. Analytic approaches to improving equity are presented using social computational big data, fairness in machine learning algorithms, medical claims data, and data augmentation as illustrations. Throughout, we emphasize the biasing influence of data absenteeism and positionality and conclude with recommendations for incorporating an equity lens into big data research.

Purpose

To validate a machine learning approach to Virtual intensity-modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.

Methods

A Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input.

Results

The methodology predicted passing rates within 3% accuracy for all composite plans measured using diode-array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per-beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under-response in low-dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.

Conclusions

We have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process.

To develop a patient-specific 'big data' clinical decision tool to predict pneumonitis in stage I non-small cell lung cancer (NSCLC) patients after stereotactic body radiation therapy (SBRT). 61 features were recorded for 201 consecutive patients with stage I NSCLC treated with SBRT, in whom 8 (4.0%) developed radiation pneumonitis. Pneumonitis thresholds were found for each feature individually using decision stumps. The performance of three different algorithms (Decision Trees, Random Forests, RUSBoost) was evaluated. Learning curves were developed and the training error analyzed and compared to the testing error in order to evaluate the factors needed to obtain a cross-validated error smaller than 0.1. These included the addition of new features, increasing the complexity of the algorithm and enlarging the sample size and number of events. In the univariate analysis, the most important feature selected was the diffusion capacity of the lung for carbon monoxide (DLCO adj%). On multivariate analysis, the three most important features selected were the dose to 15 cc of the heart, dose to 4 cc of the trachea or bronchus, and race. Higher accuracy could be achieved if the RUSBoost algorithm was used with regularization. To predict radiation pneumonitis within an error smaller than 10%, we estimate that a sample size of 800 patients is required. Clinically relevant thresholds that put patients at risk of developing radiation pneumonitis were determined in a cohort of 201 stage I NSCLC patients treated with SBRT. The consistency of these thresholds can provide radiation oncologists with an estimate of their reliability and may inform treatment planning and patient counseling. The accuracy of the classification is limited by the number of patients in the study and not by the features gathered or the complexity of the algorithm.

Background

Despite today's advances in the treatment of cancer, breast cancer-related mortality remains high, in part due to the lack of effective targeted therapies against breast tumor types that do not respond to standard treatments. Therefore, identifying additional breast cancer molecular targets is urgently needed. Super-enhancers are large regions of open chromatin involved in the overactivation of oncogenes. Thus, inhibition of super-enhancers has become a focus in clinical trials for its therapeutic potential. Here, we aimed to identify novel super-enhancer dysregulated genes highly associated with breast cancer patients' poor prognosis and negative response to treatment.

Methods

Using existing datasets containing super-enhancer-associated genes identified in breast tumors and public databases comprising genomic and clinical information for breast cancer patients, we investigated whether highly expressed super-enhancer-associated genes correlate to breast cancer patients' poor prognosis and to patients' poor response to therapy. Our computational findings were experimentally confirmed in breast cancer cells by pharmacological SE disruption and gene silencing techniques.

Results

We bioinformatically identified two novel super-enhancer-associated genes - NSMCE2 and MAL2 - highly upregulated in breast tumors, for which high RNA levels significantly and specifically correlate with breast cancer patients' poor prognosis. Through in-vitro pharmacological super-enhancer disruption assays, we confirmed that super-enhancers upregulate NSMCE2 and MAL2 transcriptionally, and, through bioinformatics, we found that high levels of NSMCE2 strongly associate with patients' poor response to chemotherapy, especially for patients diagnosed with aggressive triple negative and HER2 positive tumor types. Finally, we showed that decreasing NSMCE2 gene expression increases breast cancer cells' sensitivity to chemotherapy treatment.

Conclusions

Our results indicate that moderating the transcript levels of NSMCE2 could improve patients' response to standard chemotherapy consequently improving disease outcome. Our approach offers a new avenue to identify a signature of tumor specific genes that are not frequently mutated but dysregulated by super-enhancers. As a result, this strategy can lead to the discovery of potential and novel pharmacological targets for improving targeted therapy and the treatment of breast cancer.

The purpose of this study was to automate regular Imaging QA procedures to become more efficient and accurate. Daily and monthly imaging QA for SRS and SBRT protocols were fully automated on a Varian linac. A three-step paradigm where the data are automatically acquired, processed, and analyzed was defined. XML scripts were written and used in developer mode in a TrueBeam linac to automatically acquire data. MATLAB R013B was used to develop an interface that could allow the data to be processed and analyzed. Hardware was developed that allowed the localization of several phantoms simultaneously on the couch. 14 KV CBCTs from the Emma phantom were obtained using a TrueBeam onboard imager as example of data acquisition and analysis. The images were acquired during two months. Artifacts were artificially introduced in the images during the reconstruction process using iTool reconstructor. Support vector machine algorithms to automatically identify each artifact were written using the Machine Learning MATLAB R2011 Toolbox. A daily imaging QA test could be performed by an experienced medical physicist in 14.3 ± 2.4 min. The same test, if automated using our paradigm, could be performed in 4.2 ± 0.7 min. In the same manner, a monthly imaging QA could be performed by a physicist in 70.7 ± 8.0 min and, if fully automated, in 21.8 ± 0.6 min. Additionally, quantitative data analysis could be automatically performed by Machine Learning Algorithms that could remove the subjectivity of data interpretation in the QA process. For instance, support vector machine algorithms could correctly identify beam hardening, rings and scatter artifacts. Traditional metrics, as well as metrics that describe texture, are needed for the classification. Modern linear accelerators are equipped with advanced 2D and 3D imaging capabilities that are used for patient alignment, substantially improving IGRT treatment accuracy. However, this extra complexity exponentially increases the number of QA tests needed. Using the new paradigm described above, not only the bare minimum — but also best practice — QA programs could be implemented with the same manpower.

Purpose

Salvage high-dose-rate brachytherapy (sHDRBT) for locally recurrent prostate cancer after definitive radiation is associated with biochemical control in approximately half of patients at 3 to 5 years. Given potential toxicity, patient selection is critical. We present our institutional experience with sHDRBT and validate a recursive partitioning machines model for biochemical control.

Materials and methods

We performed a retrospective analysis of 129 patients who underwent whole-gland sHDRBT between 1998 and 2016. We evaluated clinical factors associated with biochemical control as well as toxicity.

Results

At diagnosis the median prostate-specific antigen (PSA) was 7.77 ng/mL. A majority of patients had T1-2 (73%) and Gleason 6-7 (82%) disease; 71% received external beam radiation therapy (RT) alone, and 22% received permanent prostate implants. The median disease-free interval (DFI) was 56 months, and median presalvage PSA was 4.95 ng/mL. At sHDRBT, 46% had T3 disease and 51% had Gleason 8 to 10 disease. At a median of 68 months after sHDRBT, 3- and 5-year disease-free survival were 85% (95% CI, 79-91) and 71% (95% CI, 62-79), respectively. Median PSA nadir was 0.18 ng/mL, achieved a median of 10 months after sHDRBT. Patients with ≥35%+ cores and a DFI <4.1 years had worse biochemical control (19% vs 50%, P = .02). Local failure (with or without regional/distant failure) was seen in 11% of patients (14/129), and 14 patients (11%) developed acute urinary obstruction requiring Foley placement and 19 patients (15%) developed strictures requiring dilation.

Conclusions

sHDRBT is a reasonable option for patients with locally recurrent prostate cancer after definitive RT. Those with <35%+ cores or an initial DFI of ≥4.1 years may be more likely to achieve long-term disease control after sHDRBT.

Background and purpose

Chest 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 methods

Twenty-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.

Results

Univariate 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.

Conclusions

Using 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.

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.