Evaluering av maskinlæringsmetoder for automatisk tumorsegmentering

The definition of target volumes and organs at risk (OARs) is a critical part of radiotherapy planning. In routine practice, this is typically done manually by clinical experts who contour the structures in medical images prior to dosimetric planning. This is a time-consuming and labor-intensive task. Moreover, manual contouring is inherently a subjective task and substantial contour variability can occur, potentially impacting on radiotherapy treatment and image-derived biomarkers. Automatic segmentation (auto-segmentation) of target volumes and OARs has the potential to save time and resources while reducing contouring variability. Recently, auto-segmentation of OARs using machine learning methods has been integrated into the clinical workflow by several institutions and such tools have been made commercially available by major vendors. The use of machine learning methods for auto-segmentation of target volumes including the gross tumor volume (GTV) is less mature at present but is the focus of extensive ongoing research. The primary aim of this thesis was to investigate the use of machine learning methods for auto-segmentation of the GTV in medical images. Manual GTV contours constituted the ground truth in the analyses. Volumetric overlap and distance-based metrics were used to quantify auto-segmentation performance. Four different image datasets were evaluated. The first dataset, analyzed in papers I–II, consisted of positron emission tomography (PET) and contrast-enhanced computed tomography (ceCT) images of 197 patients with head and neck cancer (HNC). The ceCT images of this dataset were also included in paper IV. Two datasets were analyzed separately in paper III, namely (i) PET, ceCT, and low-dose CT (ldCT) images of 86 patients with anal cancer (AC), and (ii) PET, ceCT, ldCT, and T2 and diffusion-weighted (T2W and DW, respectively) MR images of a subset (n = 36) of the aforementioned AC patients. The last dataset consisted of ceCT images of 36 canine patients with HNC and was analyzed in paper IV. In paper I, three approaches to auto-segmentation of the GTV in patients with HNC were evaluated and compared, namely conventional PET thresholding, classical machine learning algorithms, and deep learning using a 2-dimensional (2D) U-Net convolutional neural network (CNN). For the latter two approaches the effect of imaging modality on auto-segmentation performance was also assessed. Deep learning based on multimodality PET/ceCT image input resulted in superior agreement with the manual ground truth contours, as quantified by geometric overlap and distance-based performance evaluation metrics calculated on a per patient basis. Moreover, only deep learning provided adequate performance for segmentation based solely on ceCT images. For segmentation based on PET-only, all three approaches provided adequate segmentation performance, though deep learning ranked first, followed by classical machine learning, and PET thresholding. In paper II, deep learning-based auto-segmentation of the GTV in patients with HNC using a 2D U-Net architecture was evaluated more thoroughly by introducing new structure-based performance evaluation metrics and including qualitative expert evaluation of the resulting auto-segmentation quality. As in paper I, multimodal PET/ceCT image input provided superior segmentation performance, compared to the single modality CNN models. The structure-based metrics showed quantitatively that the PET signal was vital for the sensitivity of the CNN models, as the superior PET/ceCT-based model identified 86 % of all malignant GTV structures whereas the ceCT-based model only identified 53 % of these structures. Furthermore, the majority of the qualitatively evaluated auto-segmentations (~ 90 %) generated by the best PET/ceCT-based CNN were given a quality score corresponding to substantial clinical value. Based on papers I and II, deep learning with multimodality PET/ceCT image input would be the recommended approach for auto-segmentation of the GTV in human patients with HNC. In paper III, deep learning-based auto-segmentation of the GTV in patients with AC was evaluated for the first time, using a 2D U-Net architecture. Furthermore, an extensive comparison of the impact of different single modality and multimodality combinations of PET, ceCT, ldCT, T2W, and/or DW image input on quantitative auto-segmentation performance was conducted. For both the 86-patient and 36-patient datasets, the models based on PET/ceCT provided the highest mean overlap with the manual ground truth contours. For this task, however, comparable auto-segmentation quality was obtained for solely ceCT-based CNN models. The CNN model based solely on T2W images also obtained acceptable auto-segmentation performance and was ranked as the second-best single modality model for the 36-patient dataset. These results indicate that deep learning could prove a versatile future tool for auto-segmentation of the GTV in patients with AC. Paper IV investigated for the first time the applicability of deep learning-based auto-segmentation of the GTV in canine patients with HNC, using a 3-dimensional (3D) U-Net architecture and ceCT image input. A transfer learning approach where CNN models were pre-trained on the human HNC data and subsequently fine-tuned on canine data was compared to training models from scratch on canine data. These two approaches resulted in similar auto-segmentation performances, which on average was comparable to the overlap metrics obtained for ceCT-based auto-segmentation in human HNC patients. Auto-segmentation in canine HNC patients appeared particularly promising for nasal cavity tumors, as the average overlap with manual contours was 25 % higher for this subgroup, compared to the average for all included tumor sites. In conclusion, deep learning with CNNs provided high-quality GTV autosegmentations for all datasets included in this thesis. In all cases, the best-performing deep learning models resulted in an average overlap with manual contours which was comparable to the reported interobserver agreements between human experts performing manual GTV contouring for the given cancer type and imaging modality. Based on these findings, further investigation of deep learning-based auto-segmentation of the GTV in the given diagnoses would be highly warranted.Definisjon av målvolum og risikoorganer er en kritisk del av planleggingen av strålebehandling. I praksis gjøres dette vanligvis manuelt av kliniske eksperter som tegner inn strukturenes konturer i medisinske bilder før dosimetrisk planlegging. Dette er en tids- og arbeidskrevende oppgave. Manuell inntegning er også subjektiv, og betydelig variasjon i inntegnede konturer kan forekomme. Slik variasjon kan potensielt påvirke strålebehandlingen og bildebaserte biomarkører. Automatisk segmentering (auto-segmentering) av målvolum og risikoorganer kan potensielt spare tid og ressurser samtidig som konturvariasjonen reduseres. Autosegmentering av risikoorganer ved hjelp av maskinlæringsmetoder har nylig blitt implementert som del av den kliniske arbeidsflyten ved flere helseinstitusjoner, og slike verktøy er kommersielt tilgjengelige hos store leverandører av medisinsk teknologi. Auto-segmentering av målvolum inkludert tumorvolumet gross tumor volume (GTV) ved hjelp av maskinlæringsmetoder er per i dag mindre teknologisk modent, men dette området er fokus for omfattende pågående forskning. Hovedmålet med denne avhandlingen var å undersøke bruken av maskinlæringsmetoder for auto-segmentering av GTV i medisinske bilder. Manuelle GTVinntegninger utgjorde grunnsannheten (the ground truth) i analysene. Mål på volumetrisk overlapp og avstand mellom sanne og predikerte konturer ble brukt til å kvantifisere kvaliteten til de automatisk genererte GTV-konturene. Fire forskjellige bildedatasett ble evaluert. Det første datasettet, analysert i artikkel I–II, bestod av positronemisjonstomografi (PET) og kontrastforsterkede computertomografi (ceCT) bilder av 197 pasienter med hode/halskreft. ceCT-bildene i dette datasettet ble også inkludert i artikkel IV. To datasett ble analysert separat i artikkel III, nemlig (i) PET, ceCT og lavdose CT (ldCT) bilder av 86 pasienter med analkreft, og (ii) PET, ceCT, ldCT og T2- og diffusjonsvektet (henholdsvis T2W og DW) MR-bilder av en undergruppe (n = 36) av de ovennevnte analkreftpasientene. Det siste datasettet, som bestod av ceCT-bilder av 36 hunder med hode/halskreft, ble analysert i artikkel IV

IMAGE PROCESSING, SEGMENTATION AND MACHINE LEARNING MODELS TO CLASSIFY AND DELINEATE TUMOR VOLUMES TO SUPPORT MEDICAL DECISION

Techniques for processing and analysing images and medical data have become the main’s translational applications and researches in clinical and pre-clinical environments. The advantages of these techniques are the improvement of diagnosis accuracy and the assessment of treatment response by means of quantitative biomarkers in an efficient way. In the era of the personalized medicine, an early and efficacy prediction of therapy response in patients is still a critical issue. In radiation therapy planning, Magnetic Resonance Imaging (MRI) provides high quality detailed images and excellent soft-tissue contrast, while Computerized Tomography (CT) images provides attenuation maps and very good hard-tissue contrast. In this context, Positron Emission Tomography (PET) is a non-invasive imaging technique which has the advantage, over morphological imaging techniques, of providing functional information about the patient’s disease. In the last few years, several criteria to assess therapy response in oncological patients have been proposed, ranging from anatomical to functional assessments. Changes in tumour size are not necessarily correlated with changes in tumour viability and outcome. In addition, morphological changes resulting from therapy occur slower than functional changes. Inclusion of PET images in radiotherapy protocols is desirable because it is predictive of treatment response and provides crucial information to accurately target the oncological lesion and to escalate the radiation dose without increasing normal tissue injury. For this reason, PET may be used for improving the Planning Treatment Volume (PTV). Nevertheless, due to the nature of PET images (low spatial resolution, high noise and weak boundary), metabolic image processing is a critical task. The aim of this Ph.D thesis is to develope smart methodologies applied to the medical imaging field to analyse different kind of problematic related to medical images and data analysis, working closely to radiologist physicians. Various issues in clinical environment have been addressed and a certain amount of improvements has been produced in various fields, such as organs and tissues segmentation and classification to delineate tumors volume using meshing learning techniques to support medical decision. In particular, the following topics have been object of this study: • Technique for Crohn’s Disease Classification using Kernel Support Vector Machine Based; • Automatic Multi-Seed Detection For MR Breast Image Segmentation; • Tissue Classification in PET Oncological Studies; • KSVM-Based System for the Definition, Validation and Identification of the Incisinal Hernia Reccurence Risk Factors; • A smart and operator independent system to delineate tumours in Positron Emission Tomography scans; 3 • Active Contour Algorithm with Discriminant Analysis for Delineating Tumors in Positron Emission Tomography; • K-Nearest Neighbor driving Active Contours to Delineate Biological Tumor Volumes; • Tissue Classification to Support Local Active Delineation of Brain Tumors; • A fully automatic system of Positron Emission Tomography Study segmentation. This work has been developed in collaboration with the medical staff and colleagues at the: • Dipartimento di Biopatologia e Biotecnologie Mediche e Forensi (DIBIMED), University of Palermo • Cannizzaro Hospital of Catania • Istituto di Bioimmagini e Fisiologia Molecolare (IBFM) Centro Nazionale delle Ricerche (CNR) of Cefalù • School of Electrical and Computer Engineering at Georgia Institute of Technology The proposed contributions have produced scientific publications in indexed computer science and medical journals and conferences. They are very useful in terms of PET and MRI image segmentation and may be used daily as a Medical Decision Support Systems to enhance the current methodology performed by healthcare operators in radiotherapy treatments. The future developments of this research concern the integration of data acquired by image analysis with the managing and processing of big data coming from a wide kind of heterogeneous sources

Learning Algorithms for Fat Quantification and Tumor Characterization

Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice

Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images

Positron emission tomography (PET)-Computed tomography (CT) plays an important role in cancer management. As a multi-modal imaging technique it provides both functional and anatomical information of tumor spread. Such information improves cancer treatment in many ways. One important usage of PET-CT in cancer treatment is to facilitate radiotherapy planning, for the information it provides helps radiation oncologists to better target the tumor region. However, currently most tumor delineations in radiotherapy planning are performed by manual segmentation, which consumes a lot of time and work. Most computer-aided algorithms need a knowledgeable user to locate roughly the tumor area as a starting point. This is because, in PET-CT imaging, some tissues like heart and kidney may also exhibit a high level of activity similar to that of a tumor region. In order to address this issue, a novel co-segmentation method is proposed in this work to enhance the accuracy of tumor segmentation using PET-CT, and a localization algorithm is developed to differentiate and segment tumor regions from normal regions. On a combined dataset containing 29 patients with lung tumor, the combined method shows good segmentation results as well as good tumor recognition rate

Advanced machine learning methods for oncological image analysis

Cancer is a major public health problem, accounting for an estimated 10 million deaths worldwide in 2020 alone. Rapid advances in the field of image acquisition and hardware development over the past three decades have resulted in the development of modern medical imaging modalities that can capture high-resolution anatomical, physiological, functional, and metabolic quantitative information from cancerous organs. Therefore, the applications of medical imaging have become increasingly crucial in the clinical routines of oncology, providing screening, diagnosis, treatment monitoring, and non/minimally- invasive evaluation of disease prognosis. The essential need for medical images, however, has resulted in the acquisition of a tremendous number of imaging scans. Considering the growing role of medical imaging data on one side and the challenges of manually examining such an abundance of data on the other side, the development of computerized tools to automatically or semi-automatically examine the image data has attracted considerable interest. Hence, a variety of machine learning tools have been developed for oncological image analysis, aiming to assist clinicians with repetitive tasks in their workflow. This thesis aims to contribute to the field of oncological image analysis by proposing new ways of quantifying tumor characteristics from medical image data. Specifically, this thesis consists of six studies, the first two of which focus on introducing novel methods for tumor segmentation. The last four studies aim to develop quantitative imaging biomarkers for cancer diagnosis and prognosis. The main objective of Study I is to develop a deep learning pipeline capable of capturing the appearance of lung pathologies, including lung tumors, and integrating this pipeline into the segmentation networks to leverage the segmentation accuracy. The proposed pipeline was tested on several comprehensive datasets, and the numerical quantifications show the superiority of the proposed prior-aware DL framework compared to the state of the art. Study II aims to address a crucial challenge faced by supervised segmentation models: dependency on the large-scale labeled dataset. In this study, an unsupervised segmentation approach is proposed based on the concept of image inpainting to segment lung and head- neck tumors in images from single and multiple modalities. The proposed autoinpainting pipeline shows great potential in synthesizing high-quality tumor-free images and outperforms a family of well-established unsupervised models in terms of segmentation accuracy. Studies III and IV aim to automatically discriminate the benign from the malignant pulmonary nodules by analyzing the low-dose computed tomography (LDCT) scans. In Study III, a dual-pathway deep classification framework is proposed to simultaneously take into account the local intra-nodule heterogeneities and the global contextual information. Study IV seeks to compare the discriminative power of a series of carefully selected conventional radiomics methods, end-to-end Deep Learning (DL) models, and deep features-based radiomics analysis on the same dataset. The numerical analyses show the potential of fusing the learned deep features into radiomic features for boosting the classification power. Study V focuses on the early assessment of lung tumor response to the applied treatments by proposing a novel feature set that can be interpreted physiologically. This feature set was employed to quantify the changes in the tumor characteristics from longitudinal PET-CT scans in order to predict the overall survival status of the patients two years after the last session of treatments. The discriminative power of the introduced imaging biomarkers was compared against the conventional radiomics, and the quantitative evaluations verified the superiority of the proposed feature set. Whereas Study V focuses on a binary survival prediction task, Study VI addresses the prediction of survival rate in patients diagnosed with lung and head-neck cancer by investigating the potential of spherical convolutional neural networks and comparing their performance against other types of features, including radiomics. While comparable results were achieved in intra- dataset analyses, the proposed spherical-based features show more predictive power in inter-dataset analyses. In summary, the six studies incorporate different imaging modalities and a wide range of image processing and machine-learning techniques in the methods developed for the quantitative assessment of tumor characteristics and contribute to the essential procedures of cancer diagnosis and prognosis

Deep Learning-based Radiomics Framework for Multi-Modality PET-CT Images

Multimodal positron emission tomography - computed tomography (PET-CT) imaging is widely regarded as the imaging modality of choice for cancer management. This is because PET-CT combines the high sensitivity of PET in detecting regions of abnormal functions and the specificity of CT in depicting the underlying anatomy of where the abnormal functions are occurring. Radiomics is an emerging research field that enables the extraction and analysis of quantitative features from medical images, providing valuable insights into the underlying pathophysiology that cannot be discerned by the naked eyes. This information is capable of assisting decision-making in clinical practice, leading to better personalised treatment planning, patient outcome prediction, and therapy response assessment. The aim of this thesis is to propose a new deep learning-based radiomics framework for multimodal PET-CT images. The proposed framework comprises of three methods: 1) a tumour segmentation method via a self-supervision enabled false positive and false negative reduction network; 2) a constrained hierarchical multi-modality feature learning is constructed to predict the patient outcome with multimodal PET-CT images; 3) an automatic neural architecture search method to automatically find the optimal network architecture for both patient outcome prediction and tumour segmentation. Extensive experiments have been conducted on three datasets, including one public soft-tissue sarcomas dataset, one public challenge dataset, and one in-house lung cancer data. The results demonstrated that the proposed methods obtained better performance in all tasks when compared to the state-of-the-art methods

Investigation of intra-tumour heterogeneity to identify texture features to characterise and quantify neoplastic lesions on imaging

The aim of this work was to further our knowledge of using imaging data to discover image derived biomarkers and other information about the imaged tumour. Using scans obtained from multiple centres to discover and validate the models has advanced earlier research and provided a platform for further larger centre prospective studies. This work consists of two major studies which are describe separately: STUDY 1: NSCLC Purpose The aim of this multi-center study was to discover and validate radiomics classifiers as image-derived biomarkers for risk stratification of non-small-cell lung cancer (NSCLC). Patients and methods Pre-therapy PET scans from 358 Stage I–III NSCLC patients scheduled for radical radiotherapy/chemoradiotherapy acquired between October 2008 and December 2013 were included in this seven-institution study. Using a semiautomatic threshold method to segment the primary tumors, radiomics predictive classifiers were derived from a training set of 133 scans using TexLAB v2. Least absolute shrinkage and selection operator (LASSO) regression analysis allowed data dimension reduction and radiomics feature vector (FV) discovery. Multivariable analysis was performed to establish the relationship between FV, stage and overall survival (OS). Performance of the optimal FV was tested in an independent validation set of 204 patients, and a further independent set of 21 (TESTI) patients. Results Of 358 patients, 249 died within the follow-up period [median 22 (range 0–85) months]. From each primary tumor, 665 three-dimensional radiomics features from each of seven gray levels were extracted. The most predictive feature vector discovered (FVX) was independent of known prognostic factors, such as stage and tumor volume, and of interest to multi-center studies, invariant to the type of PET/CT manufacturer. Using the median cut-off, FVX predicted a 14-month survival difference in the validation cohort (N = 204, p = 0.00465; HR = 1.61, 95% CI 1.16–2.24). In the TESTI cohort, a smaller cohort that presented with unusually poor survival of stage I cancers, FVX correctly indicated a lack of survival difference (N = 21, p = 0.501). In contrast to the radiomics classifier, clinically routine PET variables including SUVmax, SUVmean and SUVpeak lacked any prognostic information. Conclusion PET-based radiomics classifiers derived from routine pre-treatment imaging possess intrinsic prognostic information for risk stratification of NSCLC patients to radiotherapy/chemo-radiotherapy. STUDY 2: Ovarian Cancer Purpose The 5-year survival of epithelial ovarian cancer is approximately 35-40%, prompting the need to develop additional methods such as biomarkers for personalised treatment. Patient and Methods 657 texture features were extracted from the CT scans of 364 untreated EOC patients. A 4-texture feature ‘Radiomic Prognostic Vector (RPV)’ was developed using machine learning methods on the training set. Results The RPV was able to identify the 5% of patients with the worst prognosis, significantly improving established prognostic methods and was further validated in two independent, multi-centre cohorts. In addition, the genetic, transcriptomic and proteomic analysis from two independent datasets demonstrated that stromal and DNA damage response pathways are activated in RPV-stratified tumours. Conclusion RPV could be used to guide personalised therapy of EOC. Overall, the two large datasets of different imaging modalities have increased our knowledge of texture analysis, improving the models currently available and provided us with more areas with which to implement these tools in the clinical setting.Open Acces

Multi-Modality Automatic Lung Tumor Segmentation Method Using Deep Learning and Radiomics

Delineation of the tumor volume is the initial and fundamental step in the radiotherapy planning process. The current clinical practice of manual delineation is time-consuming and suffers from observer variability. This work seeks to develop an effective automatic framework to produce clinically usable lung tumor segmentations. First, to facilitate the development and validation of our methodology, an expansive database of planning CTs, diagnostic PETs, and manual tumor segmentations was curated, and an image registration and preprocessing pipeline was established. Then a deep learning neural network was constructed and optimized to utilize dual-modality PET and CT images for lung tumor segmentation. The feasibility of incorporating radiomics and other mechanisms such as a tumor volume-based stratification scheme for training/validation/testing were investigated to improve the segmentation performance. The proposed methodology was evaluated both quantitatively with similarity metrics and clinically with physician reviews. In addition, external validation with an independent database was also conducted. Our work addressed some of the major limitations that restricted clinical applicability of the existing approaches and produced automatic segmentations that were consistent with the manually contoured ground truth and were highly clinically-acceptable according to both the quantitative and clinical evaluations. Both novel approaches of implementing a tumor volume-based training/validation/ testing stratification strategy as well as incorporating voxel-wise radiomics feature images were shown to improve the segmentation performance. The results showed that the proposed method was effective and robust, producing automatic lung tumor segmentations that could potentially improve both the quality and consistency of manual tumor delineation

Quantifying, Understanding and Predicting Differences Between Planned and Delivered Dose to Organs at Risk in Head & Neck Cancer Patients Undergoing Radical Radiotherapy to Promote Intelligently Targeted Adaptive Radiotherapy

Introduction: Radical radiotherapy (RT) is an effective but toxic treatment for head and neck cancer (HNC). Contemporary radiotherapy techniques sculpt dose to target disease and avoid organs at risk (OARs), but anatomical change during treatment mean that the radiation dose delivered to the patient – delivered dose (DA), is different to that anticipated at planning – planned dose (DP). Modifying the RT plan during treatment – Adaptive Radiotherapy (ART) – could mitigate these risks by reducing dose to OARs. However, clinical data to guide patient selection for, and timing of ART, are for lacking. Methods: 337 patients with HNC were recruited to the Cancer Research UK VoxTox study. Demographic, disease and treatment data were collated, and both DP and DA to organs at risk (OARs) were computed from daily megavoltage CT image guidance scans, using an open-source deformable image registration package (Elastix). Toxicity data were prospectively collected. Relationships between DP, DA and late toxicities were investigated with univariate, and logistic regression normal tissue complication probability (NTCP) modelling approaches. A sub-study of VoxTox recruited 18 patients who had MRI scans before RT fractions 1, 6, 16, and 26. Changes in salivary gland volumes and relative apparent diffusion coefficient (ADC) values were measured and related to toxicity events. Results: Spinal cord dose differences were small, and not predicted by weight loss or shape change. Mean DA to all other OARs was higher than DP; factors predicting higher DA included primary disease site, concomitant therapy, shape change and advanced neck disease. Nine patients (3.7%) saw DA>DP by 2Gy to more than half of the OARs assessed. These patients all had received bilateral neck RT for N-stage 2b oropharyngeal cancer. Strong uni- and multivariate relationships between OAR dose and toxicity were seen. Differences between DA and DP-based dose-toxicity models were minimal, and not statistically significant. On MRI, both parotid and submandibular glands shrank during treatment, whilst relative ADC rose. Relationships with toxicity were inconclusive. Conclusions: Small differences between OAR DP and DA mean that DA-based toxicity prediction models confer negligible additional benefit at the population level. Factors such as primary disease sub-site, concomitant systemic therapy, staging and shape change may help to select the patients that do develop clinically significant dose differences, and would benefit most from ART for toxicity reduction