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Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.
Subject(s)
Image Processing, Computer-Assisted , Radiomics , Humans , Reproducibility of Results , Biomarkers , Multimodal ImagingABSTRACT
Purpose: Data are limited on radiation-induced lung toxicities (RILT) after multiple courses of lung stereotactic body radiation therapy (SBRT). We herein analyze a large cohort of patients to explore the clinical and dosimetric risk factors associated with RILT in such settings. Methods and Materials: A single institutional database of patients treated with multiple courses of lung SBRT between January 2014 and December 2019 was analyzed. Grade 2 or higher (G2+) RILT after the last course of SBRT was the primary endpoint. Composite plans were generated with advanced algorithms including deformable registration and equivalent dose adjustment. Logistic regression analyses were performed to examine correlations between patient or treatment factors including dosimetry and G2+ RILT. Risk stratification of patients and lung constraints based on acceptable normal tissue complication probability were calculated based on risk factors identified. Results: Among 110 eligible patients (56 female and 54 male), there were 64 synchronous (58.2%; defined as 2 courses of SBRT delivered within 30 days) and 46 metachronous (41.8%) courses of SBRT. The composite median lung V20, lung V5, and mean lung dose were 9.9% (interquartile range [IQR], 7.3%-12.4%), 32.2% (IQR, 25.5%-40.1%), and 7.0 Gy (IQR, 5.5 Gy-8.6 Gy), respectively. With a median follow-up of 21.1 months, 30 patients (27.3%) experienced G2+ RILT. Five patients (4.5%) developed G3 RILT, and 1 patient (0.9%) developed G4 RILT, and no patients developed G5 RILT. On multivariable regression analysis, female sex (odds ratio [OR], 4.35; 95% CI, 1.49%-14.3%; P = .01), synchronous SBRT (OR, 8.78; 95% CI, 2.27%-47.8%; P = .004), prior G2+ RILT (OR, 29.8; 95% CI, 2.93%-437%; P = .007) and higher composite lung V20 (OR, 1.18; 95% CI, 1.02%-1.38%; P = .030) were associated with significantly higher likelihood of G2+ RILT. Conclusions: Our data suggest an acceptable incidence of G2+ RILT after multiple courses of lung SBRT. Female sex, synchronous SBRT, prior G2+ RILT, and higher composite lung V20 may be risk factors for G2+ RILT.
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BACKGROUND AND OBJECTIVES: Radiotherapy prescriptions currently derive from population-wide guidelines established through large clinical trials. We provide an open-source software tool for patient-specific prescription determination using personalized dose-response curves. METHODS: We developed ROE, a plugin to the Computational Environment for Radiotherapy Research to visualize predicted tumor control and normal tissue complication simultaneously, as a function of prescription dose. ROE can be used natively with MATLAB and is additionally made accessible in GNU Octave and Python, eliminating the need for commercial licenses. It provides a curated library of published and validated predictive models and incorporates clinical restrictions on normal tissue outcomes. ROE additionally provides batch-mode tools to evaluate and select among different fractionation schemes and analyze radiotherapy outcomes across patient cohorts. CONCLUSION: ROE is an open-source, GPL-copyrighted tool for interactive exploration of the dose-response relationship to aid in radiotherapy planning. We demonstrate its potential clinical relevance in (1) improving patient awareness by quantifying the risks and benefits of a given treatment protocol (2) assessing the potential for dose escalation across patient cohorts and (3) estimating accrual rates of new protocols.
Subject(s)
Neoplasms , Radiotherapy Planning, Computer-Assisted , Humans , Radiotherapy Planning, Computer-Assisted/methods , Software , Neoplasms/radiotherapy , Radiotherapy Dosage , PrescriptionsABSTRACT
PURPOSE: Our objective was to use interpretable machine learning for choosing dose-volume constraints on cardiopulmonary substructures (CPSs) associated with overall survival (OS) in radiation therapy for locally advanced non-small cell lung cancer. METHODS AND MATERIALS: A total of 428 patients with non-small cell lung cancer were randomly divided into training/validation/test subsets (n = 230/149/49) in Radiation Therapy Oncology Group 0617. Manual or automated contouring was performed to segment CPSs, including heart, atria, ventricles, aorta, left/right ventricle/atrium (LV+RV+LA+RA), inferior/superior vena cava, pulmonary artery, and pericardium. Peri (pericardium-heart), rest (heart-[LV+RV+LA+RA]), clinical target volume (CTV), and lungs-CTV contours were also obtained. Dose-volume histogram features were extracted, including minimum/mean dose to the hottest x% volume (Dx%[Gy]/MOHx%[Gy]), minimum/mean/maximum dose, percent volume receiving at least xGy (VxGy[%]), and overlapping volume of each CPS with planning target volume (PTV_Voverlap[%]). Clinical parameters were collected from the National Clinical Trials Network/Community oncology research program data archive. Feature selection was performed using a series of multiblock sparse partial least squares regression, stability selection supervised principal component analysis, and Boruta. Explainable boosting machine (EBM) was trained using a conditional survival distribution-based approach for imputing censored data, treating survival analysis as a regression problem. Harrell's C-index was used to evaluate OS discrimination performance of EBM, Cox proportional hazards (CPH), random survival forest, extreme gradient boosting survival embeddings, and CPH deep neural network (DeepSurv) models in the test set. Dose-volume constraints were selected using the binary change point detection algorithm in Shapley additive explanations-based partial dependence functions. RESULTS: Selected features included LA_V60Gy(%), pericardium_D30%(Gy), lungs-CTV_PTV_Voverlap(%), RA_V55Gy(%), and received_cons_chemo. All models ranked LA_V60Gy(%) as the most important feature. EBM achieved the best performance for predicting OS, followed by extreme gradient boosting survival embeddings, random survival forest, DeepSurv, and CPH (C-index = 0.653, 0.646, 0.642, 0.638, and 0.632). EBM global explanations suggested that LA_V60Gy(%) < 25.6, lungs-CTV_PTV_Voverlap(%) < 1.1, pericardium_D30%(Gy) < 18.9, RA_V55Gy(%) < 19.5, and received_cons_chemo = 'Yes' for improved OS. CONCLUSIONS: EBM can be used to discriminate OS while also guiding dose-volume constraint selection for optimal management of cardiac toxicity in lung cancer radiation therapy.
Subject(s)
Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Humans , Vena Cava, Superior , Radiotherapy Dosage , Heart Atria , Radiation DosageABSTRACT
We describe a dataset from patients who received ablative radiation therapy for locally advanced pancreatic cancer (LAPC), consisting of computed tomography (CT) and cone-beam CT (CBCT) images with physician-drawn organ-at-risk (OAR) contours. The image datasets (one CT for treatment planning and two CBCT scans at the time of treatment per patient) were collected from 40 patients. All scans were acquired with the patient in the treatment position and in a deep inspiration breath-hold state. Six radiation oncologists delineated the gastrointestinal OARs consisting of small bowel, stomach and duodenum, such that the same physician delineated all image sets belonging to the same patient. Two trained medical physicists further edited the contours to ensure adherence to delineation guidelines. The image and contour files are available in DICOM format and are publicly available from The Cancer Imaging Archive ( https://doi.org/10.7937/TCIA.ESHQ-4D90 , Version 2). The dataset can serve as a criterion standard for evaluating the accuracy and reliability of deformable image registration and auto-segmentation algorithms, as well as a training set for deep-learning-based methods.
Subject(s)
Pancreatic Neoplasms , Radiotherapy Planning, Computer-Assisted , Humans , Cone-Beam Computed Tomography/methods , Image Processing, Computer-Assisted/methods , Pancreatic Neoplasms/diagnostic imaging , Pancreatic Neoplasms/radiotherapy , Radiotherapy Planning, Computer-Assisted/methods , Reproducibility of Results , Tomography, X-Ray ComputedABSTRACT
Objective.Delineating swallowing and chewing structures aids in radiotherapy (RT) treatment planning to limit dysphagia, trismus, and speech dysfunction. We aim to develop an accurate and efficient method to automate this process.Approach.CT scans of 242 head and neck (H&N) cancer patients acquired from 2004 to 2009 at our institution were used to develop auto-segmentation models for the masseters, medial pterygoids, larynx, and pharyngeal constrictor muscle using DeepLabV3+. A cascaded framework was used, wherein models were trained sequentially to spatially constrain each structure group based on prior segmentations. Additionally, an ensemble of models, combining contextual information from axial, coronal, and sagittal views was used to improve segmentation accuracy. Prospective evaluation was conducted by measuring the amount of manual editing required in 91 H&N CT scans acquired February-May 2021.Main results. Medians and inter-quartile ranges of Dice similarity coefficients (DSC) computed on the retrospective testing set (N = 24) were 0.87 (0.85-0.89) for the masseters, 0.80 (0.79-0.81) for the medial pterygoids, 0.81 (0.79-0.84) for the larynx, and 0.69 (0.67-0.71) for the constrictor. Auto-segmentations, when compared to two sets of manual segmentations in 10 randomly selected scans, showed better agreement (DSC) with each observer than inter-observer DSC. Prospective analysis showed most manual modifications needed for clinical use were minor, suggesting auto-contouring could increase clinical efficiency. Trained segmentation models are available for research use upon request viahttps://github.com/cerr/CERR/wiki/Auto-Segmentation-models.Significance.We developed deep learning-based auto-segmentation models for swallowing and chewing structures in CT and demonstrated its potential for use in treatment planning to limit complications post-RT. To the best of our knowledge, this is the only prospectively-validated deep learning-based model for segmenting chewing and swallowing structures in CT. Segmentation models have been made open-source to facilitate reproducibility and multi-institutional research.
Subject(s)
Deep Learning , Head and Neck Neoplasms , Deglutition , Humans , Mastication , Organs at Risk , Radiotherapy Planning, Computer-Assisted/methods , Reproducibility of Results , Retrospective Studies , Tomography, X-Ray Computed/methodsABSTRACT
Purpose: The goal of this study is to develop innovative methods for identifying radiomic features that are reproducible over varying image acquisition settings. Approach: We propose a regularized partial correlation network to identify reliable and reproducible radiomic features. This approach was tested on two radiomic feature sets generated using two different reconstruction methods on computed tomography (CT) scans from a cohort of 47 lung cancer patients. The largest common network component between the two networks was tested on phantom data consisting of five cancer samples. To further investigate whether radiomic features found can identify phenotypes, we propose a k -means clustering algorithm coupled with the optimal mass transport theory. This approach following the regularized partial correlation network analysis was tested on CT scans from 77 head and neck squamous cell carcinoma (HNSCC) patients in the Cancer Imaging Archive (TCIA) and validated using an independent dataset. Results: A set of common radiomic features was found in relatively large network components between the resultant two partial correlation networks resulting from a cohort of lung cancer patients. The reliability and reproducibility of those radiomic features were further validated on phantom data using the Wasserstein distance. Further analysis using the network-based Wasserstein k -means algorithm on the TCIA HNSCC data showed that the resulting clusters separate tumor subsites as well as HPV status, and this was validated on an independent dataset. Conclusion: We showed that a network-based analysis enables identifying reproducible radiomic features and use of the selected set of features can enhance clustering results.
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Introduction: Liner dissociation of the pinnacle total hip arthroplasty system is a rare but documented complication. Although a few reports are published internationally, to the best of our knowledge no cases have been documented from India so far. Case Report: A 31-year-old male presented with failed femoral head fracture fixation for which total hip replacement was done. Postoperatively at 18 months, he was diagnosed with pinnacle liner dissociation and liner exchange was performed. Conclusion: This report aims to raise awareness about the incidence of pinnacle liner dissociation.
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Background: To investigate the impact of alpha-2-macroglobulin (A2M), a suspected intrinsic radioprotectant, on radiation pneumonitis and esophagitis using multifactorial predictive models. Materials and Methods: Baseline A2M levels were obtained for 258 patients prior to thoracic radiotherapy (RT). Dose-volume characteristics were extracted from treatment plans. Spearman's correlation (Rs) test was used to correlate clinical and dosimetric variables with toxicities. Toxicity prediction models were built using least absolute shrinkage and selection operator (LASSO) logistic regression on 1,000 bootstrapped datasets. Results: Grade ≥2 esophagitis and pneumonitis developed in 61 (23.6%) and 36 (14.0%) patients, respectively. The median A2M level was 191 mg/dL (range: 94-511). Never/former/current smoker status was 47 (18.2%)/179 (69.4%)/32 (12.4%). We found a significant negative univariate correlation between baseline A2M levels and esophagitis (Rs = -0.18/p = 0.003) and between A2M and smoking status (Rs = 0.13/p = 0.04). Further significant parameters for grade ≥2 esophagitis included age (Rs = -0.32/p < 0.0001), chemotherapy use (Rs = 0.56/p < 0.0001), dose per fraction (Rs = -0.57/p < 0.0001), total dose (Rs = 0.35/p < 0.0001), and several other dosimetric variables with Rs > 0.5 (p < 0.0001). The only significant non-dosimetric parameter for grade ≥2 pneumonitis was sex (Rs = -0.32/p = 0.037) with higher risk for women. For pneumonitis D15 (lung) (Rs = 0.19/p = 0.006) and D45 (heart) (Rs = 0.16/p = 0.016) had the highest correlation. LASSO models applied on the validation data were statistically significant and resulted in areas under the receiver operating characteristic curve of 0.84 (esophagitis) and 0.78 (pneumonitis). Multivariate predictive models did not require A2M to reach maximum predictive power. Conclusion: This is the first study showing a likely association of higher baseline A2M values with lower risk of radiation esophagitis and with smoking status. However, the baseline A2M level was not a significant risk factor for radiation pneumonitis.
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PURPOSE: To identify whether radiomic features from pre-treatment computed tomography (CT) scans can predict molecular differences between head and neck squamous cell carcinoma (HNSCC) using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). METHODS: 77 patients from the TCIA with HNSCC had imaging suitable for analysis. Radiomic features were extracted and unsupervised consensus clustering was performed to identify subtypes. Genomic data was extracted from the matched patients in the TCGA database. We explored relationships between radiomic features and molecular profiles of tumors, including the tumor immune microenvironment. A machine learning method was used to build a model predictive of CD8 + T-cells. An independent cohort of 83 HNSCC patients was used to validate the radiomic clusters. RESULTS: We initially extracted 104 two-dimensional radiomic features, and after feature stability tests and removal of volume dependent features, reduced this to 67 features for subsequent analysis. Consensus clustering based on these features resulted in two distinct clusters. The radiomic clusters differed by primary tumor subsite (p = 0.0096), HPV status (p = 0.0127), methylation-based clustering results (p = 0.0025), and tumor immune microenvironment. A random forest model using radiomic features predicted CD8 + T-cells independent of HPV status with R2 = 0.30 (p < 0.0001) on cross validation. Consensus clustering on the validation cohort resulted in two distinct clusters that differ in tumor subsite (p = 1.3 × 10-7) and HPV status (p = 4.0 × 10-7). CONCLUSION: Radiomic analysis can identify biologic features of tumors such as HPV status and T-cell infiltration and may be able to provide other information in the near future to help with patient stratification.
Subject(s)
Genomics/methods , Radiometry/methods , Squamous Cell Carcinoma of Head and Neck/radiotherapy , Female , Humans , Male , Middle AgedABSTRACT
Public preregistration of study analysis plans (SAPs) is widely recognized for clinical trials, but adopted to a much lesser extent in observational studies. Registration of SAPs prior to analysis is encouraged to not only increase transparency and exactness but also to avoid positive finding bias and better standardize outcome modeling. Efforts to generally standardize outcome modeling, which can be based on clinical trial and/or observational data, have recently spurred. We suggest a three-step SAP concept in which investigators are encouraged to (1) Design the SAP and circulate it among the co-investigators, (2) Log the SAP with a public repository, which recognizes the SAP with a digital object identifier (DOI), and (3) Cite (using the DOI), briefly summarize and motivate any deviations from the SAP in the associated manuscript. More specifically, the SAP should include the scope (brief data and study description, co-investigators, hypotheses, primary outcome measure, study title), in addition to step-by-step details of the analysis (handling of missing data, resampling, defined significance level, statistical function, validation, and variables and parameterization).
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An open-source library of implementations for deep-learning-based image segmentation and outcomes models based on radiotherapy and radiomics is presented. As oncology treatment planning becomes increasingly driven by automation, such a library of model implementations is crucial to (i) validate existing models on datasets collected at different institutions, (ii) automate segmentation, (iii) create ensembles for improving performance and (iv) incorporate validated models in the clinical workflow. Inclusion of deep-learning-based image segmentation and outcomes models in the same library provides a fully automated and reproduceable pipeline to estimate prognosis. The library was developed with the Computational Environment for Radiological Research (CERR) software platform. Centralizing model implementations in CERR builds upon its rich set of radiotherapy and radiomics tools and caters to the world-wide user base. CERR provides well-validated feature extraction pipelines for radiotherapy dosimetry and radiomics with fine control over the calculation settings, allowing users to select appropriate parameters used in model derivation. Models for automatic image segmentation are distributed via containers, allowing them to be deployed with a variety of scientific computing architectures. The library includes implementations of popular DVH-based models outlined in the Quantitative Analysis of Normal Tissue Effects in the Clinic effort and recently published literature. Radiomics models include features from the Image Biomarker Standardization Initiative and application-specific features found to be relevant across multiple sites and image modalities. The library is distributed as a module within CERR at https://www.github.com/cerr/CERR under the GNU-GPL copyleft with additional restrictions on clinical and commercial use and provision to dual license in future.
Subject(s)
Deep Learning , Image Processing, Computer-Assisted/methods , Reproducibility of ResultsABSTRACT
The Wasserstein distance is a powerful metric based on the theory of optimal mass transport. It gives a natural measure of the distance between two distributions with a wide range of applications. In contrast to a number of the common divergences on distributions such as Kullback-Leibler or Jensen-Shannon, it is (weakly) continuous, and thus ideal for analyzing corrupted and noisy data. Until recently, however, no kernel methods for dealing with nonlinear data have been proposed via the Wasserstein distance. In this work, we develop a novel method to compute the L2-Wasserstein distance in reproducing kernel Hilbert spaces (RKHS) called kernel L2-Wasserstein distance, which is implemented using the kernel trick. The latter is a general method in machine learning employed to handle data in a nonlinear manner. We evaluate the proposed approach in identifying computed tomography (CT) slices with dental artifacts in head and neck cancer, performing unsupervised hierarchical clustering on the resulting Wasserstein distance matrix that is computed on imaging texture features extracted from each CT slice. We further compare the performance of kernel Wasserstein distance with alternatives including kernel Kullback-Leibler divergence we previously developed. Our experiments show that the kernel approach outperforms classical non-kernel approaches in identifying CT slices with artifacts.
Subject(s)
Algorithms , Artifacts , Machine Learning , Normal Distribution , Tomography, X-Ray ComputedABSTRACT
BACKGROUND AND PURPOSE: Acute pain during weekly radiotherapy (RT) to the head and neck is not well characterized. We studied dose-volume metrics and clinical variables that are plausibly associated with throat or esophageal pain as measured with a weekly questionnaire during RT. MATERIALS AND METHODS: We prospectively collected weekly patient-reported outcomes from 122 head and neck cancer patients during RT. The pain score for each question consisted of a four-level scale: none (0), mild (1), moderate (2), and severe (3). Univariate and multivariate ordinal logistic regression analyses were performed to investigate associations between both esophageal and throat pain and clinical as well as dosimetric variables. RESULTS: In multivariate analysis, age was significantly associated with both types of pain, leading to odds ratio (OR)â¯=â¯0.95 (pâ¯=â¯0.008) and ORâ¯=â¯0.95 (pâ¯=â¯0.007) for esophageal and throat pain, respectively. For throat pain, sex (ORâ¯=â¯4.12; pâ¯=â¯0.010), with females at higher risk, and fractional organ at risk (OAR) mean dose (ORâ¯=â¯3.30; pâ¯=â¯0.014) were significantly associated with throat pain. CONCLUSIONS: A fractional OAR mean dose of 1.1â¯Gy seems a reasonable cutoff for separating no or mild pain from moderate to severe esophageal and throat pain. Younger patients who received RT experienced more esophageal and throat pain. Females experienced more throat pain, but not esophageal pain.
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PURPOSE: Radiomics is a growing field of image quantitation, but it lacks stable and high-quality software systems. We extended the capabilities of the Computational Environment for Radiological Research (CERR) to create a comprehensive, open-source, MATLAB-based software platform with an emphasis on reproducibility, speed, and clinical integration of radiomics research. METHOD: The radiomics tools in CERR were designed specifically to quantitate medical images in combination with CERR's core functionalities of radiological data import, transformation, management, image segmentation, and visualization. CERR allows for batch calculation and visualization of radiomics features, and provides a user-friendly data structure for radiomics metadata. All radiomics computations are vectorized for speed. Additionally, a test suite is provided for reconstruction and comparison with radiomics features computed using other software platforms such as the Insight Toolkit (ITK) and PyRadiomics. CERR was evaluated according to the standards defined by the Image Biomarker Standardization Initiative. CERR's radiomics feature calculation was integrated with the clinically used MIM software using its MATLAB® application programming interface. RESULTS: The CERR provides a comprehensive computational platform for radiomics analysis. Matrix formulations for the compute-intensive Haralick texture resulted in speeds that are superior to the implementation in ITK 4.12. For an image discretized into 32 bins, CERR achieved a speedup of 3.5 times over ITK. The CERR test suite enabled the successful identification of programming errors as well as genuine differences in radiomics definitions and calculations across the software packages tested. CONCLUSION: The CERR's radiomics capabilities are comprehensive, open-source, and fast, making it an attractive platform for developing and exploring radiomics signatures across institutions. The ability to both choose from a wide variety of radiomics implementations and to integrate with a clinical workflow makes CERR useful for retrospective as well as prospective research analyses.
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BACKGROUND: To determine if reduced dose delivery uncertainty is associated with daily image-guidance (IG) and Prostate Specific Antigen Relapse Free Survival (PRFS) in intensity-modulated radiotherapy (IMRT) of high-risk prostate cancer (PCa). METHODS: Planning data for consecutive PCa patients treated with IMRT (n = 67) and IG-IMRT (n = 35) was retrieved. Using computer simulations of setup errors, we estimated the patient-specific uncertainty in accumulated treatment dose distributions for the prostate and for posterolateral aspects of the gland that are at highest risk for extra-capsular disease. Multivariate Cox regression for PRFS considering Gleason score, T-stage, pre-treatment PSA, number of elevated clinical risk factors (T2c+, GS7+ and PSA10+), nomogram-predicted risk of extra-capsular disease (ECD), and dose metrics was performed. RESULTS: For IMRT vs. IG-IMRT, plan dosimetry values were similar, but simulations revealed uncertainty in delivered dose external to the prostate was significantly different, due to positioning uncertainties. A patient-specific interaction term of the risk of ECD and risk of low dose to the ECD (p = 0.005), and the number of elevated clinical risk factors (p = 0.008), correlate with reduced PRFS. CONCLUSIONS: Improvements in PSA outcomes for high-risk PCa using IG-IMRT vs. IMRT without IG may be due to improved dosimetry for ECD.
Subject(s)
Prostatic Neoplasms/radiotherapy , Radiotherapy Planning, Computer-Assisted/methods , Radiotherapy, Image-Guided/methods , Humans , Male , Prostate-Specific Antigen/blood , Prostatic Neoplasms/pathology , Radiotherapy, Intensity-Modulated/methodsABSTRACT
Here we develop a tool to predict resectability of HER2+ breast cancer at breast conservation surgery (BCS) utilizing features identified on preoperative breast MRI. We identified patients with HER2+ breast cancer who obtained pre-operative breast MRI and underwent BCS between 2002-2013. From the contoured tumor on pre-operative MRI, shape, histogram, and co-occurrence and size zone matrix texture features were extracted. In univariate analysis, Spearman's correlation coefficient (Rs) was used to assess the correlation between each image feature and an endpoint (surgical re-excision). For multivariate modeling, we employed a support vector machine (SVM) method in a manner of leave-one-out cross-validation (LOOCV). Of 109 patients with HER2+breast cancer who underwent BCS, 39% underwent surgical re-excision. 62% had residual cancer at re-excision. In univariate analysis, solidity (Rs = -0.32, p = 0.009) and extent (Rs = -0.29, p = 0.019) were significantly associated with re-excision. Skewness in post-contrast 1, 2, and 3 (Rs = 0.25, p = 0.045; Rs = 0.30, p = 0.015; Rs = 0.28, p = 0.026) and kurtosis in post-contrast 1 (Rs = 0.26, p = 0.035) were also statistically significant. LOOCV-based SVM test achieved 74.4% specificity and 71.4% sensitivity when 21 features were used. Thus, tumor texture, histogram and morphological MRI features may assist surgical planning, encouraging wide margins or mastectomy in patients who may otherwise go on to re-excision.
Subject(s)
Breast Neoplasms/diagnostic imaging , Magnetic Resonance Imaging/methods , Mastectomy, Segmental/adverse effects , Postoperative Complications/epidemiology , Receptor, ErbB-2/genetics , Adult , Aged , Breast Neoplasms/genetics , Breast Neoplasms/surgery , Female , Humans , Mastectomy, Segmental/methods , Middle Aged , Postoperative Complications/diagnostic imaging , Predictive Value of Tests , Support Vector MachineABSTRACT
OBJECTIVE: The aim of this study was to determine if optimized imaging protocols across multiple computed tomography (CT) vendors could result in reproducible radiomic features calculated from an anthropomorphic phantom. METHODS: Materials with varying degrees of heterogeneity were placed throughout the lungs of the phantom. Twenty scans of the phantom were acquired on 3 CT manufacturers with chest CT protocols that had optimized protocol parameters. Scans were reconstructed using vendor-specific standards and lung kernels. The concordance correlation coefficient (CCC) was used to calculate reproducibility between features. For features with high CCC values, Bland-Altman analysis was also used to quantify agreement. RESULTS: The mean Hounsfield unit (HU) was 32.93 HU (141.7 to -26.5 HU) for the rubber insert and 347.2 HU (-320.9 to -347.7 HU) for the wood insert. Low CCC values of less than 0.9 were calculated for all features across all scans. CONCLUSIONS: Radiomic features that are derived from the spatial distribution of voxel intensities should be particularly scrutinized for reproducibility in a multivendor environment.
Subject(s)
Phantoms, Imaging , Tomography Scanners, X-Ray Computed , Tomography, X-Ray Computed , Humans , Lung , Reproducibility of ResultsABSTRACT
In this study, we investigate the use of imaging feature-based outcomes research ('radiomics') combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified. From pre-treatment PET scans, 24 representative imaging features of FDG-avid disease regions were extracted. Using machine learning-based feature selection methods, multiparameter logistic regression models were built incorporating clinical factors and imaging features. All model building methods were tested by cross validation to avoid overfitting, and final outcome models were validated on an independent dataset from a collaborating institution. Multiparameter models were statistically significant on 5 fold cross validation with the area under the receiver operating characteristic curve (AUC) = 0.65 (p = 0.004), 0.73 (p = 0.026), and 0.66 (p = 0.015) for ACM, LF, and DM, respectively. The model for LF retained significance on the independent validation cohort with AUC = 0.68 (p = 0.029) whereas the models for ACM and DM did not reach statistical significance, but resulted in comparable predictive power to the 5 fold cross validation with AUC = 0.60 (p = 0.092) and 0.65 (p = 0.062), respectively. In the largest study of its kind to date, predictive features including increasing metabolic tumor volume, increasing image heterogeneity, and increasing tumor surface irregularity significantly correlated to mortality, LF, and DM on 5 fold cross validation in a relatively uniform single-institution cohort. The LF model also retained significance in an independent population.
Subject(s)
Chemoradiotherapy , Fluorodeoxyglucose F18 , Models, Statistical , Oropharyngeal Neoplasms/diagnostic imaging , Oropharyngeal Neoplasms/therapy , Positron-Emission Tomography , Adult , Aged , Aged, 80 and over , Female , Humans , Logistic Models , Male , Middle Aged , Neoplasm Metastasis , Oropharyngeal Neoplasms/pathology , ROC Curve , Treatment Outcome , Tumor BurdenABSTRACT
Deformable image registration (DIR) is essential for adaptive radiotherapy (RT) for tumor sites subject to motion, changes in tumor volume, as well as changes in patient normal anatomy due to weight loss. Several methods have been published to evaluate DIR-related uncertainties but they are not widely adopted. The aim of this study was, therefore, to evaluate intra-patient DIR for two highly deformable organs-the bladder and the rectum-in prostate cancer RT using a quantitative metric based on multiple image registration, the distance discordance metric (DDM). Voxel-by-voxel DIR uncertainties of the bladder and rectum were evaluated using DDM on weekly CT scans of 38 subjects previously treated with RT for prostate cancer (six scans/subject). The DDM was obtained from group-wise B-spline registration of each patient's collection of repeat CT scans. For each structure, registration uncertainties were derived from DDM-related metrics. In addition, five other quantitative measures, including inverse consistency error (ICE), transitivity error (TE), Dice similarity (DSC) and volume ratios between corresponding structures from pre- and post- registered images were computed and compared with the DDM. The DDM varied across subjects and structures; DDMmean of the bladder ranged from 2 to 13 mm and from 1 to 11 mm for the rectum. There was a high correlation between DDMmean of the bladder and the rectum (Pearson's correlation coefficient, R p = 0.62). The correlation between DDMmean and the volume ratios post-DIR was stronger (R p = 0.51; 0.68) than the correlation with the TE (bladder: R p = 0.46; rectum: R p = 0.47), or the ICE (bladder: R p = 0.34; rectum: R p = 0.37). There was a negative correlation between DSC and DDMmean of both the bladder (R p = -0.23) and the rectum (R p = -0.63). The DDM uncertainty metric indicated considerable DIR variability across subjects and structures. Our results show a stronger correlation with volume ratios and with the DSC using DDM compared to using ICE and TE. The DDM has the potential to quantitatively identify regions of large DIR uncertainties and consequently identify anatomical/scan outliers. The DDM can, thus, be applied to improve the adaptive RT process for tumor sites subject to motion.