Novel autosegmentation spatial similarity metrics capture the time required to correct segmentations better than traditional metrics in a thoracic cavity segmentation workflow

Automated segmentation templates can save clinicians time compared to de novo segmentation but may still take substantial time to review and correct. It has not been thoroughly investigated which automated segmentation-corrected segmentation similarity metrics best predict clinician correction time. Bilateral thoracic cavity volumes in 329 CT scans were segmented by a UNet-inspired deep learning segmentation tool and subsequently corrected by a fourth-year medical student. Eight spatial similarity metrics were calculated between the automated and corrected segmentations and associated with correction times using Spearman\u27s rank correlation coefficients. Nine clinical variables were also associated with metrics and correction times using Spearman\u27s rank correlation coefficients or Mann-Whitney U tests. The added path length, false negative path length, and surface Dice similarity coefficient correlated better with correction time than traditional metrics, including the popular volumetric Dice similarity coefficient (respectively ρ = 0.69, ρ = 0.65, ρ =  - 0.48 versus ρ =  - 0.25; correlation p values \u3c 0.001). Clinical variables poorly represented in the autosegmentation tool\u27s training data were often associated with decreased accuracy but not necessarily with prolonged correction time. Metrics used to develop and evaluate autosegmentation tools should correlate with clinical time saved. To our knowledge, this is only the second investigation of which metrics correlate with time saved. Validation of our findings is indicated in other anatomic sites and clinical workflows. Novel spatial similarity metrics may be preferable to traditional metrics for developing and evaluating autosegmentation tools that are intended to save clinicians time

Correction for fast pseudo-diffusive fluid motion contaminations in diffusion tensor imaging

In this prospective study, we quantified the fast pseudo-diffusion contamination by blood perfusion or cerebrospinal fluid (CSF) intravoxel incoherent movements on the measurement of the diffusion tensor metrics in healthy brain tissue. Diffusion-weighted imaging (TR/TE = 4100 ms/90 ms; b-values: 0, 5, 10, 20, 35, 55, 80, 110, 150, 200, 300, 500, 750, 1000, 1300 s/mm2, 20 diffusion-encoding directions) was performed on a cohort of five healthy volunteers at 3 Tesla. The projections of the diffusion tensor along each diffusion-encoding direction were computed using a two b-value approach (2b), by fitting the signal to a monoexponential curve (mono), and by correcting for fast pseudo-diffusion compartments using the biexponential intravoxel incoherent motion model (IVIM) (bi). Fractional Anisotropy (FA) and Mean Diffusivity (MD) of the diffusion tensor were quantified in regions of interest drawn over white matter areas, gray matter areas, and the ventricles. A significant dependence of the MD from the evaluation method was found in all selected regions. A lower MD was computed when accounting for the fast-diffusion compartments. A larger dependence was found in the nucleus caudatus (bi: median 0.86 10-3 mm2/s, Δ2b: -11.2%, Δmono: -14.4%; p = 0.007), in the anterior horn (bi: median 2.04 10-3 mm2/s, Δ2b: -9.4%, Δmono: -11.5%, p = 0.007) and in the posterior horn of the lateral ventricles (bi: median 2.47 10-3 mm2/s, Δ2b: -5.5%, Δmono: -11.7%; p = 0.007). Also for the FA, the signal modeling affected the computation of the anisotropy metrics. The deviation depended on the evaluated region with significant differences mainly in the nucleus caudatus (bi: median 0.15, Δ2b: +39.3%, Δmono: +14.7%; p = 0.022) and putamen (bi: median 0.19, Δ2b: +3.1%, Δmono: +17.3%; p = 0.015). Fast pseudo-diffusive regimes locally affect diffusion tensor imaging (DTI) metrics in the brain. Here, we propose the use of an IVIM-based method for correction of signal contaminations through CSF or perfusion

Mixed Effect Modeling of Dose and Linear Energy Transfer Correlations With Brain Image Changes After Intensity Modulated Proton Therapy for Skull Base Head and Neck Cancer

Purpose Intensity modulated proton therapy (IMPT) could yield high linear energy transfer (LET) in critical structures and increased biological effect. For head and neck cancers at the skull base this could potentially result in radiation-associated brain image change (RAIC). The purpose of the current study was to investigate voxel-wise dose and LET correlations with RAIC after IMPT. Methods and Materials For 15 patients with RAIC after IMPT, contrast enhancement observed on T1-weighted magnetic resonance imaging was contoured and coregistered to the planning computed tomography. Monte Carlo calculated dose and dose-averaged LET (LETd) distributions were extracted at voxel level and associations with RAIC were modelled using uni- and multivariate mixed effect logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve and precision-recall curve. Results An overall statistically significant RAIC association with dose and LETd was found in both the uni- and multivariate analysis. Patient heterogeneity was considerable, with standard deviation of the random effects of 1.81 (1.30-2.72) for dose and 2.68 (1.93-4.93) for LETd, respectively. Area under the receiver operating characteristic curve was 0.93 and 0.95 for the univariate dose-response model and multivariate model, respectively. Analysis of the LETd effect demonstrated increased risk of RAIC with increasing LETd for the majority of patients. Estimated probability of RAIC with LETd = 1 keV/µm was 4% (95% confidence interval, 0%, 0.44%) and 29% (95% confidence interval, 0.01%, 0.92%) for 60 and 70 Gy, respectively. The TD15 were estimated to be 63.6 and 50.1 Gy with LETd equal to 2 and 5 keV/µm, respectively. Conclusions Our results suggest that the LETd effect could be of clinical significance for some patients; LETd assessment in clinical treatment plans should therefore be taken into consideration.publishedVersio

Safety of high-dose-rate stereotactic body radiotherapy

Background and purposeFlattening filter free (FFF) beams with high dose rate are increasingly used for stereotactic body radiotherapy (SBRT), because they substantially shorten beam-on time. The physical properties of these beams together with potentially unknown radiobiological effects might affect patient safety. Therefore here we analyzed the clinical outcome of our patients.Material and methodsBetween 3/2010 and 2/2014 84 patients with 100 lesions (lung 75, liver 10, adrenal 6, lymph nodes 5, others 4) were treated with SBRT using 6 MV FFF or 10 MV FFF beams at our institution. Clinical efficacy endpoints and toxicity were assessed by Kaplan-Meier analysis and CTCAE criteria version 4.0.ResultsMedian follow-up was 11 months (range: 3¿41). No severe acute toxicity was observed. There has been one case of severe late toxicity (1%), a grade 3 bile duct stricture that was possibly related to SBRT. For all patients, the 1-year local control rate, progression free survival and overall survival were 94%, 38% and 80% respectively, and for patients with lung lesions 94%, 48% and 83%, respectively.ConclusionsNo unexpected toxicity occurred. Toxicity and treatment efficacy are perfectly in range with studies investigating SBRT with flattened beams. The use of FFF beams at maximum dose rate for SBRT is time efficient and appears to be safe

Radiation recall dermatitis induced by sorafenib : A case study and review of the literature

BACKGROUND: Radiation recall dermatitis (RRD) is an acute inflammatory reaction confined to previously irradiated skin, mainly subsequent to the administration of certain chemotherapeutics. Here we present a rare case of RRD induced by the oral multikinase inhibitor sorafenib. CASE REPORT: A 77-year-old male with hepatocellular carcinoma was irradiated at ten different sites for bone metastases with 20-36 Gray in 5-12 fractions from January to March 2015. Sorafenib 400 mg was administered twice daily from mid-March. One week later the patient presented with fever and erythematous lesions on the right upper arm, mandible, and trunk. All skin symptoms were confined to previously irradiated areas. After RRD was diagnosed by exclusion of other causes and skin biopsy, sorafenib was paused. With the administration of topical corticosteroids and oral antihistamines, the skin reaction subsided within several days. Sorafenib was readministered after 3 weeks, which did not lead to recurrence of RRD but did cause fluctuating fever. DISCUSSION: Only four other such cases have been reported in the literature and WHO pharmacovigilance database on individual case safety reports. The current report is the first to show a potential relationship between the severity of sorafenib-induced RRD and radiation dose, histopathological features, and simultaneous acute radiation dermatitis and mucositis. CONCLUSION: RRD induced by sorafenib is a rare phenomenon, but should be considered in patients showing erythematous skin lesions 1-2 weeks after initiation of the drug, predominantly in areas where skin has been irradiated with an equivalent dose ≥ 30 Gy. Discontinuation of sorafenib with possible readministration should be evaluated with respect to the clinical situation and severity of reaction

Fully automatic classification of breast MRI background parenchymal enhancement using a transfer learning approach

Marked enhancement of the fibroglandular tissue on contrast-enhanced breast magnetic resonance imaging (MRI) may affect lesion detection and classification and is suggested to be associated with higher risk of developing breast cancer. The background parenchymal enhancement (BPE) is qualitatively classified according to the BI-RADS atlas into the categories "minimal," "mild," "moderate," and "marked." The purpose of this study was to train a deep convolutional neural network (dCNN) for standardized and automatic classification of BPE categories.This IRB-approved retrospective study included 11,769 single MR images from 149 patients. The MR images were derived from the subtraction between the first post-contrast volume and the native T1-weighted images. A hierarchic approach was implemented relying on 2 dCNN models for detection of MR-slices imaging breast tissue and for BPE classification, respectively. Data annotation was performed by 2 board-certified radiologists. The consensus of the 2 radiologists was chosen as reference for BPE classification. The clinical performances of the single readers and of the dCNN were statistically compared using the quadratic Cohen's kappa.Slices depicting the breast were classified with training, validation, and real-world (test) accuracies of 98%, 96%, and 97%, respectively. Over the 4 classes, the BPE classification was reached with mean accuracies of 74% for training, 75% for the validation, and 75% for the real word dataset. As compared to the reference, the inter-reader reliabilities for the radiologists were 0.780 (reader 1) and 0.679 (reader 2). On the other hand, the reliability for the dCNN model was 0.815.Automatic classification of BPE can be performed with high accuracy and support the standardization of tissue classification in MRI