Radiomics studies require many patients in order to power them, thus patients are often combined from different institutions and using different imaging protocols. Various studies have shown that ...imaging protocols affect radiomics feature values. We examined whether using data from cohorts with controlled imaging protocols improved patient outcome models. We retrospectively reviewed 726 CT and 686 PET images from head and neck cancer patients, who were divided into training or independent testing cohorts. For each patient, radiomics features with different preprocessing were calculated and two clinical variables-HPV status and tumor volume-were also included. A Cox proportional hazards model was built on the training data by using bootstrapped Lasso regression to predict overall survival. The effect of controlled imaging protocols on model performance was evaluated by subsetting the original training and independent testing cohorts to include only patients whose images were obtained using the same imaging protocol and vendor. Tumor volume, HPV status, and two radiomics covariates were selected for the CT model, resulting in an AUC of 0.72. However, volume alone produced a higher AUC, whereas adding radiomics features reduced the AUC. HPV status and one radiomics feature were selected as covariates for the PET model, resulting in an AUC of 0.59, but neither covariate was significantly associated with survival. Limiting the training and independent testing to patients with the same imaging protocol reduced the AUC for CT patients to 0.55, and no covariates were selected for PET patients. Radiomics features were not consistently associated with survival in CT or PET images of head and neck patients, even within patients with the same imaging protocol.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Manually delineating upper abdominal organs at risk (OARs) is a time-consuming task. To develop a deep-learning-based tool for accurate and robust auto-segmentation of these OARs, forty pancreatic ...cancer patients with contrast-enhanced breath-hold computed tomographic (CT) images were selected. We trained a three-dimensional (3D) U-Net ensemble that automatically segments all organ contours concurrently with the self-configuring nnU-Net framework. Our tool's performance was assessed on a held-out test set of 30 patients quantitatively. Five radiation oncologists from three different institutions assessed the performance of the tool using a 5-point Likert scale on an additional 75 randomly selected test patients. The mean (± std. dev.) Dice similarity coefficient values between the automatic segmentation and the ground truth on contrast-enhanced CT images were 0.80 ± 0.08, 0.89 ± 0.05, 0.90 ± 0.06, 0.92 ± 0.03, 0.96 ± 0.01, 0.97 ± 0.01, 0.96 ± 0.01, and 0.96 ± 0.01 for the duodenum, small bowel, large bowel, stomach, liver, spleen, right kidney, and left kidney, respectively. 89.3% (contrast-enhanced) and 85.3% (non-contrast-enhanced) of duodenum contours were scored as a 3 or above, which required only minor edits. More than 90% of the other organs' contours were scored as a 3 or above. Our tool achieved a high level of clinical acceptability with a small training dataset and provides accurate contours for treatment planning.
•Deep learning models were trained to automatically delineate cardiac substructures.•Multi-class 3D nnU-Net segmentation model achieved a high segmentation accuracy.•Dosimetric evaluation shows the ...auto contours achieve an accurate plan evaluation.•Physician evaluation indicates 94% of auto-segmented contours are clinically acceptable.
Accurate and comprehensive segmentation of cardiac substructures is crucial for minimizing the risk of radiation-induced heart disease in lung cancer radiotherapy. We sought to develop and validate deep learning–based auto-segmentation models for cardiac substructures.
Nineteen cardiac substructures (whole heart, 4 heart chambers, 6 great vessels, 4 valves, and 4 coronary arteries) in 100 patients treated for non-small cell lung cancer were manually delineated by two radiation oncologists. The valves and coronary arteries were delineated as planning risk volumes. An nnU-Net auto-segmentation model was trained, validated, and tested on this dataset with a split ratio of 75:5:20. The auto-segmented contours were evaluated by comparing them with manually drawn contours in terms of Dice similarity coefficient (DSC) and dose metrics extracted from clinical plans. An independent dataset of 42 patients was used for subjective evaluation of the auto-segmentation model by 4 physicians.
The average DSCs were 0.95 (+/– 0.01) for the whole heart, 0.91 (+/– 0.02) for 4 chambers, 0.86 (+/– 0.09) for 6 great vessels, 0.81 (+/– 0.09) for 4 valves, and 0.60 (+/– 0.14) for 4 coronary arteries. The average absolute errors in mean/max doses to all substructures were 1.04 (+/– 1.99) Gy and 2.20 (+/– 4.37) Gy. The subjective evaluation revealed that 94% of the auto-segmented contours were clinically acceptable.
We demonstrated the effectiveness of our nnU-Net model for delineating cardiac substructures, including coronary arteries. Our results indicate that this model has promise for studies regarding radiation dose to cardiac substructures.
Reduced weight bearing, and to a lesser extent radiation, during spaceflight have been shown as potential hazards to astronaut joint health. These hazards combined effect to the knee and hip joints ...are not well defined, particularly with low-dose exposure to radiation. In this study, we examined the individual and combined effects of varying low-dose radiation (≤1 Gy) and reduced weight bearing on the cartilage of the knee and hip joints. C57BL/6J mice (n = 80) were either tail suspended via hindlimb unloading (HLU) or remained full-weight bearing (ground). On day 6, each group was divided and irradiated with 0 Gy (sham), 0.1 Gy, 0.5 Gy or 1.0 Gy (n = 10/group), yielding eight groups: ground-sham; ground-0.1 Gy; ground-0.5 Gy; ground-1.0 Gy; HLU-sham; HLU-0.1 Gy; HLU-0.5 Gy; and HLU-1.0 Gy. On day 30, the hindlimbs, hip cartilage and serum were collected from the mice. Significant differences were identified statistically between treatment groups and the ground-sham control group, but no significant differences were observed between HLU and/or radiation groups. Contrast-enhanced micro-computed tomography (microCECT) demonstrated decrease in volume and thickness at the weight-bearing femoral-tibial cartilage-cartilage contact point in all treatment groups compared to ground-sham. Lower collagen was observed in all groups compared to ground-sham. Circulating serum cartilage oligomeric matrix protein (sCOMP), a biomarker for ongoing cartilage degradation, was increased in all of the irradiated groups compared to ground-sham, regardless of unloading. Mass spectrometry of the cartilage lining the femoral head and subsequent Ingenuity Pathway Analysis (IPA) identified a decrease in cartilage compositional proteins indicative of osteoarthritis. Our findings demonstrate that both individually and combined, HLU and exposure to spaceflight relevant radiation doses lead to cartilage degradation of the knee and hip with expression of an arthritic phenotype. Moreover, early administration of low-dose irradiation (0.1, 0.5 or 1.0 Gy) causes an active catabolic response in cartilage 24 days postirradiation. Further research is warranted with a focus on the prevention of cartilage degradation from long-term periods of reduced weight bearing and spaceflight-relevant low doses and qualities of radiation.
Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what ...is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.
Abstract Thoracic anthropometry variations with age and sex have been reported and likely relate to thoracic injury risk and outcome. The objective of this study was to collect a large volume of ...homologous semilandmark data from the thoracic skeleton for the purpose of quantifying thoracic morphology variations for males and females of ages 0–100 years. A semi-automated image segmentation and registration algorithm was applied to collect homologous thoracic skeleton semilandmarks from 343 normal computed tomography (CT) scans. Rigid, affine, and symmetric diffeomorphic transformations were used to register semilandmarks from an atlas to homologous locations in the subject-specific coordinate system. Homologous semilandmarks were successfully collected from 92% (7077) of the ribs and 100% (187) of the sternums included in the study. Between 2700 and 11,000 semilandmarks were collected from each rib and sternum and over 55 million total semilandmarks were collected from all subjects. The extensive landmark data collected more fully characterizes thoracic skeleton morphology across ages and sexes. Characterization of thoracic morphology with age and sex may help explain variations in thoracic injury risk and has important implications for vulnerable populations such as pediatrics and the elderly.
•Statistical Process Control metrics can assess linac operational performance.•Linacs produced in series have statistically different operating parameters.•Although having different operating ...parameters, linacs have similar performance.
The purpose of this study was to determine if medical linear accelerators (linac) produced by the same manufacturer exhibit operational consistency within their subsystems and components. Two linacs that were commissioned together and installed at the same facility were monitored. Each machine delivered a daily robust quality assurance (QA) irradiation. Linacs and their components operate consistently, but have different operational parameter levels even when produced by the same manufacturer and commissioned in series. These findings have implications on the feasibility of true clinical beam matching.
Planning for palliative radiotherapy is performed without the advantage of MR or PET imaging in many clinics. Here, we investigated CT-only GTV delineation for palliative treatment of head and neck ...cancer. Two multi-institutional datasets of palliative-intent treatment plans were retrospectively acquired: a set of 102 non-contrast-enhanced CTs and a set of 96 contrast-enhanced CTs. The nnU-Net auto-segmentation network was chosen for its strength in medical image segmentation, and five approaches separately trained: (1) heuristic-cropped, non-contrast images with a single GTV channel, (2) cropping around a manually-placed point in the tumor center for non-contrast images with a single GTV channel, (3) contrast-enhanced images with a single GTV channel, (4) contrast-enhanced images with separate primary and nodal GTV channels, and (5) contrast-enhanced images along with synthetic MR images with separate primary and nodal GTV channels. Median Dice similarity coefficient ranged from 0.6 to 0.7, surface Dice from 0.30 to 0.56, and 95th Hausdorff distance from 14.7 to 19.7 mm across the five approaches. Only surface Dice exhibited statistically-significant difference across these five approaches using a two-tailed Wilcoxon Rank-Sum test (p ≤ 0.05). Our CT-only results met or exceeded published values for head and neck GTV autocontouring using multi-modality images. However, significant edits would be necessary before clinical use in palliative radiotherapy.
Purpose
Treatment planning for craniospinal irradiation (CSI) is complex and time-consuming, especially for resource-constrained centers. To alleviate demanding workflows, we successfully automated ...the pediatric CSI planning pipeline in previous work. In this work, we validated our CSI autosegmentation and autoplanning tool on a large dataset from St. Jude Children’s Research Hospital.
Methods
Sixty-three CSI patient CT scans were involved in the study. Pre-planning scripts were used to automatically verify anatomical compatibility with the autoplanning tool. The autoplanning pipeline generated 15 contours and a composite CSI treatment plan for each of the compatible test patients (n=51). Plan quality was evaluated quantitatively with target coverage and dose to normal tissue metrics and qualitatively with physician review, using a 5-point Likert scale. Three pediatric radiation oncologists from 3 institutions reviewed and scored 15 contours and a corresponding composite CSI plan for the final 51 test patients. One patient was scored by 3 physicians, resulting in 53 plans scored total.
Results
The algorithm automatically detected 12 incompatible patients due to insufficient junction spacing or head tilt and removed them from the study. Of the 795 autosegmented contours reviewed, 97% were scored as clinically acceptable, with 92% requiring no edits. Of the 53 plans scored, all 51 brain dose distributions were scored as clinically acceptable. For the spine dose distributions, 92%, 100%, and 68% of single, extended, and multiple-field cases, respectively, were scored as clinically acceptable. In all cases (major or minor edits), the physicians noted that they would rather edit the autoplan than create a new plan.
Conclusions
We successfully validated an autoplanning pipeline on 51 patients from another institution, indicating that our algorithm is robust in its adjustment to differing patient populations. We automatically generated 15 contours and a comprehensive CSI treatment plan for each patient without physician intervention, indicating the potential for increased treatment planning efficiency and global access to high-quality radiation therapy.
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