Datamining approaches for modeling tumor control probability

Abstract

Background Tumor control probability (TCP) to radiotherapy is determined by complex interactions between tumor biology, tumor microenvironment, radiation dosimetry, and patient-related variables. The complexity of these heterogeneous variable interactions constitutes a challenge for building predictive models for routine clinical practice. We describe a datamining framework that can unravel the higher order relationships among dosimetric dose-volume prognostic variables, interrogate various radiobiological processes, and generalize to unseen data before when applied prospectively. Material and methods Several datamining approaches are discussed that include dose-volume metrics, equivalent uniform dose, mechanistic Poisson model, and model building methods using statistical regression and machine learning techniques. Institutional datasets of non-small cell lung cancer (NSCLC) patients are used to demonstrate these methods. The performance of the different methods was evaluated using bivariate Spearman rank correlations (rs). Over-fitting was controlled via resampling methods. Results Using a dataset of 56 patients with primary NCSLC tumors and 23 candidate variables, we estimated GTV volume and V75 to be the best model parameters for predicting TCP using statistical resampling and a logistic model. Using these variables, the support vector machine (SVM) kernel method provided superior performance for TCP prediction with an rs = 0.68 on leave-one-out testing compared to logistic regression (rs = 0.4), Poisson-based TCP (rs = 0.33), and cell kill equivalent uniform dose model (rs = 0.17). Conclusions The prediction of treatment response can be improved by utilizing datamining approaches, which are able to unravel important non-linear complex interactions among model variables and have the capacity to predict on unseen data for prospective clinical applications.

Incidental Receipt of Cardiac Medications and Survival Outcomes Among Patients With Stage III Non–Small-Cell Lung Cancer After Definitive Radiotherapy

“…1,[6][7][8] Therefore, the discovery of novel agents or signaling pathway targets that could prevent recurrence after definitive treatment has become increasingly important. [9][10][11][12][13] Preclinical findings have implicated cardiac medications such as angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs), which are in wide use as antihypertensive agents, in tumor growth and cancer progression. [14][15][16][17][18] Some clinical investigations indicated that receipt of ACEIs or ARBs was associated with improved outcomes among patients with prostate, hepatocellular, pancreatic, breast, or bladder cancer, [19][20][21][22][23][24] but others have not.…”

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

“…Machine learning has previously been used in radiation oncology for a variety of problems, from quality assurance to outcome prediction 20, 21, 22, 23, 24, 25, 26. In circumstances where the event being analyzed is relatively uncommon, machine learning algorithms are advantageous in magnifying events.…”

Modeling of Tumor Control Probability (TCP)

“…Four regions are shown in the figure representing high/low risks of local failure with high/low confidence levels, respectively. Note that cases falling within the classification margin have low confidence prediction power and represent intermediate-risk patients, i.e., patients with "border-like" characteristics that could belong to either risk group [26].…”

“…Separable cases could be modeled by linear kernels while non-separable cases are modeled by nonlinear kernels that allow for separability of the data but at the expense of increased dimensionality. This step could be preceded by a variable selection process and the generalizability of the model is evaluated using resampling techniques as discussed below [26].…”