Where Position Matters-Deep-Learning-Driven Normalization and Coregistration of Computed Tomography in the Postoperative Analysis of Deep Brain Stimulation.

INTRODUCTION: Recent developments in the postoperative evaluation of deep brain stimulation surgery on the group level warrant the detection of achieved electrode positions based on postoperative imaging. Computed tomography (CT) is a frequently used imaging modality, but because of its idiosyncrasies (high spatial accuracy at low soft tissue resolution), it has not been sufficient for the parallel determination of electrode position and details of the surrounding brain anatomy (nuclei). The common solution is rigid fusion of CT images and magnetic resonance (MR) images, which have much better soft tissue contrast and allow accurate normalization into template spaces. Here, we explored a deep-learning approach to directly relate positions (usually the lead position) in postoperative CT images to the native anatomy of the midbrain and group space. MATERIALS AND METHODS: Deep learning is used to create derived tissue contrasts (white matter, gray matter, cerebrospinal fluid, brainstem nuclei) based on the CT image; that is, a convolution neural network (CNN) takes solely the raw CT image as input and outputs several tissue probability maps. The ground truth is based on coregistrations with MR contrasts. The tissue probability maps are then used to either rigidly coregister or normalize the CT image in a deformable way to group space. The CNN was trained in 220 patients and tested in a set of 80 patients. RESULTS: Rigorous validation of such an approach is difficult because of the lack of ground truth. We examined the agreements between the classical and proposed approaches and considered the spread of implantation locations across a group of identically implanted subjects, which serves as an indicator of the accuracy of the lead localization procedure. The proposed procedure agrees well with current magnetic resonance imaging-based techniques, and the spread is comparable or even lower. CONCLUSIONS: Postoperative CT imaging alone is sufficient for accurate localization of the midbrain nuclei and normalization to the group space. In the context of group analysis, it seems sufficient to have a single postoperative CT image of good quality for inclusion. The proposed approach will allow researchers and clinicians to include cases that were not previously suitable for analysis.

Analysis of the accuracy of computer-assisted DCIA flap mandibular reconstruction applying a novel approach based on geometric morphometrics.

BACKGROUND: This study evaluated the accuracy of computer-assisted surgery (CAS)-driven DCIA (deep circumflex iliac artery) flap mandibular reconstruction by traditional morphometric methods and geometric morphometric methods (GMM). METHODS: Reconstruction accuracy was evaluated by measuring distances and angles between bilateral anatomical landmarks. Additionally, the average length of displacements vectors between landmarks was computed to evaluate factors assumed to influence reconstruction accuracy. Principal component analysis (PCA) was applied to unveil main modes of dislocation. RESULTS: High reconstruction accuracy could be demonstrated for a sample consisting of 26 patients. The effect of the number of segments and length of defect on reconstruction accuracy were close to the commonly used significance threshold (p = 0.062/0.060). PCA demonstrated displacement to result mainly from sagittal and transversal shifts. CONCLUSIONS: CAS is a viable approach to achieve high accuracy in mandibular reconstruction and GMM can facilitate the evaluation of factors influencing reconstruction accuracy and unveil main modes of dislocation in this context.

Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors.

OBJECTIVES: To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to radiologists. METHODS: In 880 patients (age 33.1 ± 19.4 years, 395 women) diagnosed with malignant (n = 213, 24.2%) or benign (n = 667, 75.8%) primary bone tumors, preoperative radiographs were obtained, and the diagnosis was established using histopathology. Data was split 70%/15%/15% for training, validation, and internal testing. Additionally, 96 patients from another institution were obtained for external testing. Machine learning models were developed and validated using radiomic features and demographic information. The performance of each model was evaluated on the test sets for accuracy, area under the curve (AUC) from receiver operating characteristics, sensitivity, and specificity. For comparison, the external test set was evaluated by two radiology residents and two radiologists who specialized in musculoskeletal tumor imaging. RESULTS: The best machine learning model was based on an artificial neural network (ANN) combining both radiomic and demographic information achieving 80% and 75% accuracy at 75% and 90% sensitivity with 0.79 and 0.90 AUC on the internal and external test set, respectively. In comparison, the radiology residents achieved 71% and 65% accuracy at 61% and 35% sensitivity while the radiologists specialized in musculoskeletal tumor imaging achieved an 84% and 83% accuracy at 90% and 81% sensitivity, respectively. CONCLUSIONS: An ANN combining radiomic features and demographic information showed the best performance in distinguishing between benign and malignant bone lesions. The model showed lower accuracy compared to specialized radiologists, while accuracy was higher or similar compared to residents. KEY POINTS: • The developed machine learning model could differentiate benign from malignant bone tumors using radiography with an AUC of 0.90 on the external test set. • Machine learning models that used radiomic features or demographic information alone performed worse than those that used both radiomic features and demographic information as input, highlighting the importance of building comprehensive machine learning models. • An artificial neural network that combined both radiomic and demographic information achieved the best performance and its performance was compared to radiology readers on an external test set.

Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs.

Background An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow. Purpose To develop a multitask deep learning (DL) model for simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. Materials and Methods This retrospective study analyzed bone tumors on radiographs acquired prior to treatment and obtained from patient data from January 2000 to June 2020. Benign or malignant bone tumors were diagnosed in all patients by using the histopathologic findings as the reference standard. By using split-sample validation, 70% of the patients were assigned to the training set, 15% were assigned to the validation set, and 15% were assigned to the test set. The final performance was evaluated on an external test set by using geographic validation, with accuracy, sensitivity, specificity, and 95% CIs being used for classification, the intersection over union (IoU) being used for bounding box placements, and the Dice score being used for segmentations. Results Radiographs from 934 patients (mean age, 33 years ± 19 [standard deviation]; 419 women) were evaluated in the internal data set, which included 667 benign bone tumors and 267 malignant bone tumors. Six hundred fifty-four patients were in the training set, 140 were in the validation set, and 140 were in the test set. One hundred eleven patients were in the external test set. The multitask DL model achieved 80.2% (89 of 111; 95% CI: 72.8, 87.6) accuracy, 62.9% (22 of 35; 95% CI: 47, 79) sensitivity, and 88.2% (67 of 76; CI: 81, 96) specificity in the classification of bone tumors as malignant or benign. The model achieved an IoU of 0.52 ± 0.34 for bounding box placements and a mean Dice score of 0.60 ± 0.37 for segmentations. The model accuracy was higher than that of two radiologic residents (71.2% and 64.9%; P = .002 and P < .001, respectively) and was comparable with that of two musculoskeletal fellowship-trained radiologists (83.8% and 82.9%; P = .13 and P = .25, respectively) in classifying a tumor as malignant or benign. Conclusion The developed multitask deep learning model allowed for accurate and simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Carrino in this issue.

Four-dimensional magnetic resonance imaging-derived ascending aortic flow eccentricity and flow compression are linked to aneurysm morphology†.

OBJECTIVES: The impact of specific blood flow patterns within ascending aortic and/or aortic root aneurysms on aortic morphology is unknown. We investigated the interrelation of ascending aortic flow compression/peripheralization and aneurysm morphology with respect to sinotubuar junction (STJ) definition. METHODS: Thirty-one patients (aortic root/ascending aortic aneurysm >45 mm) underwent flow-sensitive 4D magnetic resonance thoracic aortic flow measurement at 3 Tesla (Siemens, Germany) at two different institutions (Freiburg, Germany, and San Francisco, CA, USA). Time-resolved image data post-processing and visualization of mid-systolic, mid-ascending aortic flow were performed using local vector fields. The Flow Compression Index (FCI) was calculated individually as a fraction of the area of high-velocity mid-systolic flow over the complete cross-sectional ascending aortic area. According to aortic aneurysm morphology, patients were grouped as (i) small root, eccentric ascending aortic aneurysm (STJ definition) and (ii) enlarged aortic root, non-eccentric ascending aortic aneurysm with diffuse root and tubular enlargement. RESULTS: The mean FCI over all patients was 0.47 ± 0.5 (0.37-0.99). High levels of flow compression/peripheralization (FCI <0.6) were linked to eccentric aneurysm morphology (Group A, n = 11), while low levels or absence of aortic flow compression/peripheralization (FCI >0.8) occurred more often in Group B (n = 20). The FCI was 0.48 ± 0.05 in Group A and 0.78 ± 0.14 in Group B (P < 0.001). Distribution of bicuspid aortic valve (P = 0.6) and type of valve dysfunction (P = 0.22 for aortic stenosis) was not found to be different between groups. CONCLUSIONS: Irrespective of aortic valve morphology and function, ascending aortic blood flow patterns are linked to distinct patterns of ascending aortic aneurysm morphology. Implementation of quantitative local blood flow analyses might help to improve aneurysm risk stratification in the future.

Late complications and distal growth rates of Marfan aortas after proximal aortic repair.

OBJECTIVES: Conflicting results have been reported on late aortic growth and complication rates of the descending thoracic aorta in patients with Marfan syndrome (MFS) after proximal aortic surgery. METHODS: Of 198 Marfan patients followed up regularly, 121 (43% David-I, 7% David-II, 11% supracoronary replacement, 52% mechanical conduit, 8% arch replacement) were analysed after proximal aortic surgery retrospectively. 97% had MFS1, 3% MFS2 (Loeys-Dietz-Syndrome); 56% were male and the mean age was 35 ± 13 years. 65% were initially operated on for root/ascending aortic aneurysm and 35% for aortic dissections. Using automated computed tomography angiography and magnetic resonance angiography cross-sectional analyses, the mean diameters of the distal arch, mid-descending and distal supradiaphragmatic descending thoracic aorta were measured at early and late follow-up (mean 6.3 years for aneurysms and 4.7 years for dissections). The mean duration of clinical follow-up was 7.6 years and the cumulative clinical follow-up comprised 894 patient-years. RESULTS: At 20 years, overall freedom from distal aortic complications and/or reintervention was 76% (51-86%) for aneurysms and 52% (28-71%) for dissections (P = 0.03). In non-dissected aortas, distal aortic growth was significant, but minimal: arches grew from 25.2 ± 0.6 to 26.3 ± 0.8 mm (P = 0.01), mid-descending aortas from 22.2 ± 0.5 to 24.9 ± 1.2 mm (P = 0.05) and distal descending aortas from 22.1 ± 0.7 to 24.2 ± 1.4 (P = 0.02, 0.58 mm/year ± 0.5 mm). Dissected distal aortas increased by a mean of 0.3 ± 0.5 mm/year. Dissection (P < 0.001), urgent procedure (P = 0.02) and hypertension (0.052) were associated with larger distal aortic diameters at late follow-up and more significant aortic growth over time. CONCLUSIONS: Late distal complication rates are low for patients initially presenting with aneurysms. The risk of late distal reoperation is dictated by the initial pathology and by the presence of an initial dissection and not by faster distal aortic growth. Strategies to completely restore a non-dissected anatomy might improve late surgical outcome in Marfan's syndrome.