A neural network to predict symptomatic lung injury

Naïve Bayesian network-based contribution analysis of tumor biology and healthcare factors to racial disparity in breast cancer stage-at-diagnosis

Background

Variation in breast cancer stage at initial diagnosis (including racial disparities) is driven both by tumor biology and healthcare factors.

Methods

We studied women age 67-74 with initial diagnosis of breast cancer from 2006 through 2014 in the SEER-Medicare database. We extracted variables related to tumor biology (histologic grade and hormone receptor status) and healthcare factors (screening mammography [SM] utilization and time delay from mammography to diagnostic biopsy). We used naïve Bayesian networks (NBNs) to illustrate the relationships among patient-specific factors and stage-at-diagnosis for African American (AA) and white patients separately. After identifying and controlling confounders, we conducted counterfactual inference through the NBN, resulting in an unbiased evaluation of the causal effects of individual factors on the expected utility of stage-at-diagnosis. An NBN-based decomposition mechanism was developed to evaluate the contributions of each patient-specific factor to an actual racial disparity in stage-at-diagnosis. 2000 bootstrap samples from our training patients were used to compute the 95% confidence intervals (CIs) of these contributions.

Results

Using a causal-effect contribution analysis, the relative contributions of each patient-specific factor to the actual racial disparity in stage-at-diagnosis were as follows: tumor grade, 45.1% (95% CI: 44.5%, 45.8%); hormone receptor status, 5.0% (4.5%, 5.4%); mammography utilization, 23.1% (22.4%, 24.0%); and biopsy delay 26.8% (26.1%, 27.3%).

Conclusion

The modifiable mechanisms of mammography utilization and biopsy delay drive about 49.9% of racial difference in stage-at-diagnosis, potentially guiding more targeted interventions to eliminate cancer outcome disparities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-021-00165-5.

Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century

Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI's inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI's chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.

Machine learning and modeling: Data, validation, communication challenges

With the era of big data, the utilization of machine learning algorithms in radiation oncology is rapidly growing with applications including: treatment response modeling, treatment planning, contouring, organ segmentation, image-guidance, motion tracking, quality assurance, and more. Despite this interest, practical clinical implementation of machine learning as part of the day-to-day clinical operations is still lagging. The aim of this white paper is to further promote progress in this new field of machine learning in radiation oncology by highlighting its untapped advantages and potentials for clinical advancement, while also presenting current challenges and open questions for future research. The targeted audience of this paper includes newcomers as well as practitioners in the field of medical physics/radiation oncology. The paper also provides general recommendations to avoid common pitfalls when applying these powerful data analytic tools to medical physics and radiation oncology problems and suggests some guidelines for transparent and informative reporting of machine learning results.

Strategies for prediction and mitigation of radiation-induced liver toxicity

Although well described in the 1960s, liver toxicity secondary to radiation therapy, commonly known as radiation-induced liver disease (RILD), remains a major challenge. RILD encompasses two distinct clinical entities, a 'classic' form, composed of anicteric hepatomegaly, ascites and elevated alkaline phosphatase; and a 'non-classic' form, with liver transaminases elevated to more than five times the reference value, or worsening of liver metabolic function represented as an increase of 2 or more points in the Child-Pugh score classification. The risk of occurrence of RILD has historically limited the applicability of radiation for the treatment of liver malignancies. With the development of 3D conformal radiation therapy, which allowed for partial organ irradiation based on computed tomography treatment planning, there has been a resurgence of interest in the use of liver irradiation. Since then, a large body of evidence regarding the liver tolerance to conventionally fractionated radiation has been produced, but severe liver toxicities has continued to be reported. More recently, improvements in diagnostic imaging, radiation treatment planning technology and delivery systems have prompted the development of stereotactic body radiotherapy (SBRT), by which high doses of radiation can be delivered with high target accuracy and a steep dose gradient at the tumor - normal tissue interface, offering an opportunity of decreasing toxicity rates while improving tumor control. Here, we present an overview of the role SBRT has played in the management of liver tumors, addressing the challenges and opportunities to reduce the incidence of RILD, such as adaptive approaches and machine-learning-based predictive models.

Radiomics in precision medicine for lung cancer

With the improvement of external radiotherapy delivery accuracy, such as intensity-modulated and stereotactic body radiation therapy, radiation oncology has recently entered in the era of precision medicine. Despite these precise irradiation modalities, lung cancers remain one of the most aggressive human cancers worldwide, possibly because of diverse genotypic alterations that drive and maintain lung tumorigenesis. It has been long recognized that imaging could aid in the diagnosis, tumor delineation, and monitoring of lung cancer. Moreover, accumulating evidence suggests that imaging information could be further used to tailor treatment type and intensity, as well as predict treatment outcomes in radiotherapy. However, these imaging tasks have been carried out either qualitatively or using simplistic metrics that doesn't take advantage of the full scale of imaging knowledge. Radiomics, which is a recent field of research that aims to provide a more quantitative representation of imaging information relating tumor phenotypes to clinical and genotypic endpoints by embedding extracted image features into predictive mathematical models. These predictive models can be a key component in the clinician decision making and treatment personalization. This review provides an overview of the radiomics application and its methodology for radiation oncology studies in lung cancer.

Radiogenomics and radiotherapy response modeling

Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.

Perspectives on making big data analytics work for oncology

Oncology, with its unique combination of clinical, physical, technological, and biological data provides an ideal case study for applying big data analytics to improve cancer treatment safety and outcomes. An oncology treatment course such as chemoradiotherapy can generate a large pool of information carrying the 5Vs hallmarks of big data. This data is comprised of a heterogeneous mixture of patient demographics, radiation/chemo dosimetry, multimodality imaging features, and biological markers generated over a treatment period that can span few days to several weeks. Efforts using commercial and in-house tools are underway to facilitate data aggregation, ontology creation, sharing, visualization and varying analytics in a secure environment. However, open questions related to proper data structure representation and effective analytics tools to support oncology decision-making need to be addressed. It is recognized that oncology data constitutes a mix of structured (tabulated) and unstructured (electronic documents) that need to be processed to facilitate searching and subsequent knowledge discovery from relational or NoSQL databases. In this context, methods based on advanced analytics and image feature extraction for oncology applications will be discussed. On the other hand, the classical p (variables)≫n (samples) inference problem of statistical learning is challenged in the Big data realm and this is particularly true for oncology applications where p-omics is witnessing exponential growth while the number of cancer incidences has generally plateaued over the past 5-years leading to a quasi-linear growth in samples per patient. Within the Big data paradigm, this kind of phenomenon may yield undesirable effects such as echo chamber anomalies, Yule-Simpson reversal paradox, or misleading ghost analytics. In this work, we will present these effects as they pertain to oncology and engage small thinking methodologies to counter these effects ranging from incorporating prior knowledge, using information-theoretic techniques to modern ensemble machine learning approaches or combination of these. We will particularly discuss the pros and cons of different approaches to improve mining of big data in oncology.

Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective

Radiation oncology has always been deeply rooted in modeling, from the early days of isoeffect curves to the contemporary Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) initiative. In recent years, medical modeling for both prognostic and therapeutic purposes has exploded thanks to increasing availability of electronic data and genomics. One promising direction that medical modeling is moving toward is adopting the same machine learning methods used by companies such as Google and Facebook to combat disease. Broadly defined, machine learning is a branch of computer science that deals with making predictions from complex data through statistical models. These methods serve to uncover patterns in data and are actively used in areas such as speech recognition, handwriting recognition, face recognition, "spam" filtering (junk email), and targeted advertising. Although multiple radiation oncology research groups have shown the value of applied machine learning (ML), clinical adoption has been slow due to the high barrier to understanding these complex models by clinicians. Here, we present a review of the use of ML to predict radiation therapy outcomes from the clinician's point of view with the hope that it lowers the "barrier to entry" for those without formal training in ML. We begin by describing 7 principles that one should consider when evaluating (or creating) an ML model in radiation oncology. We next introduce 3 popular ML methods--logistic regression (LR), support vector machine (SVM), and artificial neural network (ANN)--and critique 3 seminal papers in the context of these principles. Although current studies are in exploratory stages, the overall methodology has progressively matured, and the field is ready for larger-scale further investigation.

Monte Carlo role in radiobiological modelling of radiotherapy outcomes

Radiobiological models are essential components of modern radiotherapy. They are increasingly applied to optimize and evaluate the quality of different treatment planning modalities. They are frequently used in designing new radiotherapy clinical trials by estimating the expected therapeutic ratio of new protocols. In radiobiology, the therapeutic ratio is estimated from the expected gain in tumour control probability (TCP) to the risk of normal tissue complication probability (NTCP). However, estimates of TCP/NTCP are currently based on the deterministic and simplistic linear-quadratic formalism with limited prediction power when applied prospectively. Given the complex and stochastic nature of the physical, chemical and biological interactions associated with spatial and temporal radiation induced effects in living tissues, it is conjectured that methods based on Monte Carlo (MC) analysis may provide better estimates of TCP/NTCP for radiotherapy treatment planning and trial design. Indeed, over the past few decades, methods based on MC have demonstrated superior performance for accurate simulation of radiation transport, tumour growth and particle track structures; however, successful application of modelling radiobiological response and outcomes in radiotherapy is still hampered with several challenges. In this review, we provide an overview of some of the main techniques used in radiobiological modelling for radiotherapy, with focus on the MC role as a promising computational vehicle. We highlight the current challenges, issues and future potentials of the MC approach towards a comprehensive systems-based framework in radiobiological modelling for radiotherapy.

Clinical comparative study of dose-volume and equivalent uniform dose based predictions in post radiotherapy acute complications

BACKGROUND

Single points placed on Dose-Volume Histograms (DVHs) for treatment plan acceptance are still widely used compared to the Equivalent Uniform Dose (EUD). The aim of this work is to retrospectively measure and compare the ability of both criteria in correctly predicting two clinical outcomes, RTOG grade 2 acute gastrointestinal (GI) and genitourinary (GU) complications in 137 patients treated for prostate cancer.

MATERIAL AND METHODS

For both complications,the best predictions have been achieved by fitting the EUD parameter and a tolerance dose (for a varying DVH point) by maximization of the Area Under the Receiver Operating Curve (AUROC). A complementary likelihood fitting of the Lyman's Normal Tissue Complication Probability (NTCP) allowed a graphical comparison between expected and observed frequencies, and to derive the associated parameters.

RESULTS AND DISCUSSION

No significant differences were found in the AUROC values obtained by using dose-volume or EUD criteria, but all the results highlighted the role of high doses. Limiting V65 (for grade 2 GI) or V73 (for grade 2 GU) was as predictive as limiting EUD value, with n equal to 0.09 or 0.06 respectively, but in all cases AUROC values were low (< 0.7). Likelihood fitting gave m = 0.195 and TD50=72.5 Gy (fixing n=0.06 for acute GU) and m=0.19 and TD50=66 Gy (fixing n=0.09 for acute GI). Both AUROC and likelihood values revealed a better fit for acute GI than for acute GU. The use of a fractionation correction, new clinical contours or previous risk factors could improve these values.

Predicting radiotherapy outcomes using statistical learning techniques

Radiotherapy outcomes are determined by complex interactions between treatment, anatomical and patient-related variables. A common obstacle to building maximally predictive outcome models for clinical practice is the failure to capture potential complexity of heterogeneous variable interactions and applicability beyond institutional data. We describe a statistical learning methodology that can automatically screen for nonlinear relations among prognostic variables and generalize to unseen data before. In this work, several types of linear and nonlinear kernels to generate interaction terms and approximate the treatment-response function are evaluated. Examples of institutional datasets of esophagitis, pneumonitis and xerostomia endpoints were used. Furthermore, an independent RTOG dataset was used for 'generalizabilty' validation. We formulated the discrimination between risk groups as a supervised learning problem. The distribution of patient groups was initially analyzed using principle components analysis (PCA) to uncover potential nonlinear behavior. The performance of the different methods was evaluated using bivariate correlations and actuarial analysis. Over-fitting was controlled via cross-validation resampling. Our results suggest that a modified support vector machine (SVM) kernel method provided superior performance on leave-one-out testing compared to logistic regression and neural networks in cases where the data exhibited nonlinear behavior on PCA. For instance, in prediction of esophagitis and pneumonitis endpoints, which exhibited nonlinear behavior on PCA, the method provided 21% and 60% improvements, respectively. Furthermore, evaluation on the independent pneumonitis RTOG dataset demonstrated good generalizabilty beyond institutional data in contrast with other models. This indicates that the prediction of treatment response can be improved by utilizing nonlinear kernel methods for discovering important nonlinear interactions among model variables. These models have the capacity to predict on unseen data.

A neural network model to predict lung radiation-induced pneumonitis

A feed-forward neural network was investigated to predict the occurrence of lung radiation-induced Grade 2+ pneumonitis. The database consisted of 235 patients with lung cancer treated using radiotherapy, of whom 34 were diagnosed with Grade 2+ pneumonitis at follow-up. The network was constructed using an algorithm that alternately grew and pruned it, starting from the smallest possible network, until a satisfactory solution was found. The weights and biases of the network were computed using the error back-propagation approach. Momentum and variable leaning techniques were used to speed convergence. Using the growing/pruning approach, the network selected features from 66 dose and 27 non-dose variables. During network training, the 235 patients were randomly split into ten groups of approximately equal size. Eight groups were used to train the network, one group was used for early stopping training to prevent overfitting, and the remaining group was used as a test to measure the generalization capability of the network (cross-validation). Using this methodology, each of the ten groups was considered, in turn, as the test group (ten-fold cross-validation). For the optimized network constructed with input features selected from dose and non-dose variables, the area under the receiver operating characteristics (ROC) curve for cross-validated testing was 0.76 (sensitivity: 0.68, specificity: 0.69). For the optimized network constructed with input features selected only from dose variables, the area under the ROC curve for cross-validation was 0.67 (sensitivity: 0.53, specificity: 0.69). The difference between these two areas was statistically significant (p = 0.020), indicating that the addition of non-dose features can significantly improve the generalization capability of the network. A network for prospective testing was constructed with input features selected from dose and non-dose variables (all data were used for training). The optimized network architecture consisted of six input nodes (features), four hidden nodes, and one output node. The six input features were: lung volume receiving > 16 Gy (V16), generalized equivalent uniform dose (gEUD) for the exponent a = 1 (mean lung dose), gEUD for the exponent a = 3.5, free expiratory volume in 1 s (FEV1), diffusion capacity of carbon monoxide (DLCO%), and whether or not the patient underwent chemotherapy prior to radiotherapy. The significance of each input feature was individually evaluated by omitting it during network training and gauging its impact by the consequent deterioration in cross-validated ROC area. With the exception of FEV1 and whether or not the patient underwent chemotherapy prior to radiotherapy, all input features were found to be individually significant (p < 0.05). The network for prospective testing is publicly available via internet access.

Prediction of Radiation Induced Liver Disease Using Artificial Neural Networks

OBJECTIVE

To evaluate the efficiency of predicting radiation induced liver disease (RILD) with an artificial neural network (ANN) model.

METHODS AND MATERIALS

From August 2000 to November 2004, a total of 93 primary liver carcinoma (PLC) patients with single lesion and associated with hepatic cirrhosis of Child-Pugh grade A, were treated with hypofractionated three-dimensional conformal radiotherapy (3DCRT). Eight out of 93 patients were diagnosed RILD. Ninety-three patients were randomly divided into two subsets (training set and verification set). In model A, the ratio of patient numbers was 1:1 for training and verification set, and in model B, the ratio was 2:1.

RESULTS

The areas under receiver-operating characteristic (ROC) curves were 0.8897 and 0.8831 for model A and B, respectively. Sensitivity, specificity, accuracy, positive prediction value (PPV) and negative prediction value (NPV) were 0.875 (7/8), 0.882 (75/85), 0.882 (82/93), 0.412 (7/17) and 0.987 (75/76) for model A, and 0.750 (6/8), 0.800 (68/85), 0.796 (74/93), 0.261 (6/23) and 0.971 (68/70) for model B.

CONCLUSION

ANN was proved high accuracy for prediction of RILD. It could be used together with other models and dosimetric parameters to evaluate hepatic irradiation plans.

Delayed disease progression after allogeneic cell vaccination in hormone-resistant prostate cancer and correlation with immunologic variables

PURPOSE

There are a significant number of patients with asymptomatic hormone-resistant prostate cancer who have increasing prostate-specific antigen (PSA) levels but little or no evaluable disease. The immunogenicity and minimal toxicity associated with cell-based vaccine therapy makes this approach attractive for these patients.

EXPERIMENTAL DESIGN

We have evaluated a vaccine comprising monthly intradermal injection of three irradiated allogeneic prostate cell lines (8 x 10(6) cells each) over 1 year. The first two doses were supplemented with bacille Calmette-Guérin as vaccine adjuvant. Twenty-eight hormone-resistant prostate cancer patients were enrolled. Patients were assessed clinically and PSA levels were measured monthly. Radiologic scans (X-ray, computed tomography, and bone scan) were taken at baseline and at intervals throughout the treatment period. Comprehensive monthly immunologic monitoring was undertaken including proliferation studies, activation markers, cytokine protein expression, and gene copy number. This longitudinal data was analyzed through predictive modeling using artificial neural network feed-forward/back-propagation algorithms with multilayer perceptron architecture.

RESULTS

Eleven of the 26 patients showed statistically significant, prolonged decreases in their PSA velocity (PSAV). None experienced any significant toxicity. Median time to disease progression was 58 weeks, compared with recent studies of other agents and historical control values of around 28 weeks. PSAV-responding patients showed a titratable T(H)1 cytokine release profile in response to restimulation with a vaccine lysate, while nonresponders showed a mixed T(H)1 and T(H)2 response. Furthermore, immunologic profile correlated with PSAV response by artificial neural network analysis. We found predictive power not only in expression of cytokines after maximal stimulation with phorbol 12-myristate 13-acetate, but also the method of analysis (qPCR measurement of IFN-gamma > qPCR measurement tumor necrosis factor-alpha > protein expression of IFN-gamma > protein expression of interleukin 2).

CONCLUSIONS

Whole cell allogeneic vaccination in hormone-resistant prostate cancer is nontoxic and improves the natural history of the disease. Longitudinal changes in immunologic function in vaccinated patients may be better interpreted through predictive modeling using tools such as the artificial neural network rather than periodic "snapshot" readouts.

An artificial neural network for predicting the incidence of radiation pneumonitis

A method to predict radiation-induced pneumonitis (RP) using an artificial neural network (ANN) was investigated. A retrospective study was applied to the clinical data from 142 patients who have been treated with three-dimensional conformal radiotherapy for tumors in the thoracic region. These data were classified, based on their treatment outcome, into two patient clusters: with RP (Np=26) and without RP (Np= 116). An ANN was designed as a classifier. To perform the classification, a patient-treatment outcome with RP was assigned a value of 1, and a patient treatment outcome without RP was assigned a value of -1. The input of the ANN was limited to the patient lung dose-volume data only. A volume vector (VD) that describes patient lung subvolumes receiving more than a set of threshold doses was used as the network input variable. A zero value was used as the threshold to set the output value into -1 or 1. Three ANNs (ANN_1, ANN_2, and ANN_3), each with three layers, were trained to perform this classification function and to show the effect of training data on the ANN performance. Radial basis function was applied as the hidden layer neuron activation function and a sigmoid function was selected as the output layer neuron function. Backpropagation with a conjugate gradient algorithm was used to train the network. ANN_1 was trained and tested by using the leave-one-out method. ANN_2 was trained by randomly selecting 2/3 of the patient data, and tested by the remaining 1/3 of the data. ANN_3 was trained by a user selecting 2/3 of the patient data, and tested by the remaining 1/3 of the data. The predictive accuracy was verified as the area under a receiver operator characteristic (ROC) curve. The correct classification rates of 73% for RP cases, and 99% for non-RP cases were obtained from ANN_1. The corresponding correct classification rates of 44% for RP cases, and 89% for non-RP cases were obtained from ANN_2. From the ANN_3 test phase, the corresponding correct classification rates of 55% for RP cases, and 95% non-RP cases were achieved. The area under ROC curve was 0.85+/-0.05, 0.68+/-0.10, and 0.81+/-0.09 for ANN_1, ANN _2, and ANN_3, respectively, within its asymmetric 95% confidence interval. The sensitivity was 95%, 57%, and 71%, and the specificity was 94%, 88%, and 90% for ANN_1, ANN_2, and ANN_3, respectively. Preliminary results suggest that the ANN approach provides a useful tool for the prediction of radiation-induced lung pneumonitis, using the patient lung dose-volume information.

Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate

BACKGROUND AND PURPOSE

This paper discusses the application of artificial neural networks (ANN) in predicting biological outcomes following prostate radiotherapy. A number of model-based methods have been developed to correlate the dose distributions calculated for a patient receiving radiotherapy and the radiobiological effect this will produce. Most widely used are the normal tissue complication probability and tumour control probability models. An alternative method for predicting specific examples of tumour control and normal tissue complications is to use an ANN. One of the advantages of this method is that there is no need for a priori information regarding the relationship between the data being correlated.

PATIENTS AND METHODS

A set of retrospective clinical data from patients who received radical prostate radiotherapy was used to train ANNs to predict specific biological outcomes by learning the relationship between the treatment plan prescription, dose distribution and the corresponding biological effect. The dose and volume were included as a differential dose-volume histogram in order to provide a holistic description of the available data.

RESULTS

It was shown that the ANNs were able to predict biochemical control and specific bladder and rectum complications with sensitivity and specificity of above 55% when the outcomes were dichotomised. It was also possible to analyse information from the ANNs to investigate the effect of individual treatment parameters on the outcome.

CONCLUSION

ANNs have been shown to learn something of the complex relationship between treatment parameters and outcome which, if developed further, may prove to be a useful tool in predicting biological outcomes.

Modifying normal tissue damage postirradiation. Report of a workshop sponsored by the Radiation Research Program, National Cancer Institute, Bethesda, Maryland, September 6-8, 2000

Late effects that develop in normal tissues adjacent to the tumor site in the months to years after radiotherapy can reduce the quality of life of cancer survivors. They can be dose-limiting and debilitating or life-threatening. There is now evidence that some late effects may be preventable or partially reversible. A workshop, "Modifying Normal Tissue Damage Postirradiation", was sponsored by the Radiation Research Program of the National Cancer Institute to identify the current status of and research needs and opportunities in this area. Mechanistic, genetic and physiological studies of the development of late effects are needed and will provide a rational basis for development of treatments. Interdisciplinary teams will be needed to carry out this research, including pathologists, physiologists, geneticists, molecular biologists, experts in functional imaging, wound healing, burn injury, molecular biology, and medical oncology, in addition to radiation biologists, physicists and oncologists. The participants emphasized the need for developing and choosing appropriate models, and for radiation dose-response studies to determine whether interventions remain effective at the radiation doses used clinically. Both preclinical and clinical studies require long-term follow-up, and easier-to-use, more objective clinical scoring systems must be developed and standardized. New developments in biomedical imaging should provide useful tools in all these endeavors. The ultimate goals are to improve the quality of life and efficacy of treatment for cancer patients treated with radiotherapy.