Reference values for Ductus Venosus pulsatility index between 11 and 13 + 6 weeks of Gestation: A Single-center Observational Study in a Tertiary Care Hospital

BackgroundThe ductus venosus pulsatility index(DVPI) has been evolved as an important marker of the first trimester screening sonography. The aim of this study is to define a reference for ductus venosus pulsatility index at 11–13 +6 weeks of gestation.MethodsIn this cross sectional observational study, 415 women with singleton pregnancies and crown lump length(CRL) between 45 and 84 mm were included. Exclusion criteria were abnormal biochemical screening results, presence of fetal structural malformation or chromosomal abnormalities such as thickened nuchal fold, abnormal perinatal outcomes, and newborns with a chromosomal abnormality. Transabdominal U/S was performed in all participants by an expert radiologist in obstetric sonography. CRL, nuchal translucency(NT), and blood flow indices of ductus venosus (DV) in each fetus were measured. The collected data were analyzed by IBM SPSS software version 20. Linear regression was performed to demonstrate the association between CRL DVPI. Further, 5th, 50th, and 95th percentiles of DV blood flow indices were calculated for each gestational age.ResultThe mean value of DVPI ranged from 1.05 at CRL 42mm to 1.3 at CRL 82mm. DVPI and CRL did not show any significant linear association (Regression coefficient B=0.001, R2=0.003, P=0.31)Conclusion: We defined means and ranges of DVPI, while determining the 5th, 50th, and 95th percentiles of DVPI for each CRL at our institution which were approximately similar to previous studies.

Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images

Background The 3D U-Net model has been proved to perform well in the automatic organ segmentation. The aim of this study is to evaluate the feasibility of the 3D U-Net algorithm for the automated detection and segmentation of lymph nodes (LNs) on pelvic diffusion-weighted imaging (DWI) images. Methods A total of 393 DWI images of patients suspected of having prostate cancer (PCa) between January 2019 and December 2020 were collected for model development. Seventy-seven DWI images from another group of PCa patients imaged between January 2021 and April 2021 were collected for temporal validation. Segmentation performance was assessed using the Dice score, positive predictive value (PPV), true positive rate (TPR), and volumetric similarity (VS), Hausdorff distance (HD), the Average distance (AVD), and the Mahalanobis distance (MHD) with manual annotation of pelvic LNs as the reference. The accuracy with which the suspicious metastatic LNs (short diameter > 0.8 cm) were detected was evaluated using the area under the curve (AUC) at the patient level, and the precision, recall, and F1-score were determined at the lesion level. The consistency of LN staging on an hold-out test dataset between the model and radiologist was assessed using Cohen’s kappa coefficient. Results In the testing set used for model development, the Dice score, TPR, PPV, VS, HD, AVD and MHD values for the segmentation of suspicious LNs were 0.85, 0.82, 0.80, 0.86, 2.02 (mm), 2.01 (mm), and 1.54 (mm) respectively. The precision, recall, and F1-score for the detection of suspicious LNs were 0.97, 0.98 and 0.97, respectively. In the temporal validation dataset, the AUC of the model for identifying PCa patients with suspicious LNs was 0.963 (95% CI: 0.892–0.993). High consistency of LN staging (Kappa = 0.922) was achieved between the model and expert radiologist. Conclusion The 3D U-Net algorithm can accurately detect and segment pelvic LNs based on DWI images.

Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium

Background Artificial intelligence can assist in cardiac image interpretation. Here, we achieved a substantial reduction in time required to read a cardiovascular magnetic resonance (CMR) study to estimate left atrial volume without compromising accuracy or reliability. Rather than deploying a fully automatic black-box, we propose to incorporate the automated LA volumetry into a human-centric interactive image-analysis process. Methods and results Atri-U, an automated data analysis pipeline for long-axis cardiac cine images, computes the atrial volume by: (i) detecting the end-systolic frame, (ii) outlining the endocardial borders of the LA, (iii) localizing the mitral annular hinge points and constructing the longitudinal atrial diameters, equivalent to the usual workup done by clinicians. In every step human interaction is possible, such that the results provided by the algorithm can be accepted, corrected, or re-done from scratch. Atri-U was trained and evaluated retrospectively on a sample of 300 patients and then applied to a consecutive clinical sample of 150 patients with various heart conditions. The agreement of the indexed LA volume between Atri-U and two experts was similar to the inter-rater agreement between clinicians (average overestimation of 0.8 mL/m2 with upper and lower limits of agreement of − 7.5 and 5.8 mL/m2, respectively). An expert cardiologist blinded to the origin of the annotations rated the outputs produced by Atri-U as acceptable in 97% of cases for step (i), 94% for step (ii) and 95% for step (iii), which was slightly lower than the acceptance rate of the outputs produced by a human expert radiologist in the same cases (92%, 100% and 100%, respectively). The assistance of Atri-U lead to an expected reduction in reading time of 66%—from 105 to 34 s, in our in-house clinical setting. Conclusions Our proposal enables automated calculation of the maximum LA volume approaching human accuracy and precision. The optional user interaction is possible at each processing step. As such, the assisted process sped up the routine CMR workflow by providing accurate, precise, and validated measurement results.

NIMG-54. RADIOMIC FEATURES FROM LESION HABITAT PREDICT RESPONSE TO COMBINATION OF NIVOLUMAB AND BEVACIZUMAB IN PATIENTS WITH RECURRENT GLIOBLASTOMA: A FEASIBILITY STUDY

PURPOSE The use of immunotherapy in glioblastoma management is under active investigation. Glioblastomas are “cold” tumors, meaning that they have inactivated or fewer tumor infiltrative lymphocytes in addition to substantial tumor necrosis, attributing to their poor response to immunotherapy. A significant challenge is the apriori identification of Glioblastoma patients who will respond favorably to immunotherapy. In this work, we evaluated the ability of computerized MRI-based quantitative features (radiomics) extracted from the lesion habitat (including enhancing lesion, necrosis, and peritumoral hyperintensities) to predict response and progression-free survival (PFS) in recurrent GBM patients treated with combination of Nivolumab and Bevacizumab. METHODS Immunotherapy response assessment in neuro-oncology (iRANO) criteria along with PFS were used to analyze n=50 patients enrolled in a randomized clinical trial where patients received Nivolumab with either standard or low dose Bevacizumab. These patients were assessed to see if they had complete response, partial response, stable disease (i.e. responders, n=31), or disease progression (i.e. non-responders, n=19). Lesion habitat constituting necrotic core, enhancing tumor, and edema were delineated by expert radiologist on Gd-T1w, T2w and FLAIR MRI scans. COLIAGE radiomic features from each of the delineated regions were selected using minimum redundancy maximum relevance (mRMR) via cross-validation, to segregate non-responder patients from responders. A multivariable cox proportional hazard model was used to predict survival (PFS). RESULTS CoLlAGe correlation, sum average, and sum variance features (capture local heterogeneity) from the lesion habitat, were found to segregate non-responder patients from responders with an accuracy of 86%, followed by 80% using features from peritumoral hyperintensities and 78% from enhancing tumor. In our survival analysis, C-index of 0.688 was obtained using features from the entire lesion habitat, followed by peritumoral hyperintensities (0.675) and enhancing tumor (0.656). CONCLUSION Radiomic features from the lesion habitat may predict response to combination of Nivolumab and Bevacizumab in recurrent Glioblastomas.

Adrenal wash-out CT: moderate diagnostic value in distinguishing benign from malignant adrenal masses

Objective: Reliable results of wash-out CT in the diagnostic workup of adrenal incidentalomas are scarce. Thus, we evaluated the diagnostic accuracy of delayed wash-out CT and determined thresholds to accurately differentiate adrenal masses. Design: Retrospective, single-center cohort study including 216 patients with 252 adrenal lesions who underwent delayed wash-out CT. Definitive diagnoses based on histopathology (n=92) or comprehensive follow-up. Methods: Size, average attenuation values of the adrenal lesions in all CT scan phases, absolute and relative percentage washout (APW/RPW) were determined by an expert radiologist blinded for clinical data. Adrenal lesions with unenhanced attenuation values >10HU built a subgroup (n=142). Diagnostic accuracy were calculated. Results: The study group consisted of 171 adenomas, 32 other benign tumors, 11 pheochromocytomas, 9 adrenocortical carcinomas and 29 other malignant tumors. All (potentially) malignant and 46% of benign lesions showed unenhanced attenuation values >10HU. In this most relevant subgroup, the established thresholds of 60% for APW and 40% for RPW misclassified 35.9% and 35.2% of masses, respectively. When we applied optimized cutoffs (APW>83%; RPW>58%) and excluded pheochromocytomas, we missed only 1 malignant tumor by APW and none by RPW. However, only 11% and 15% of benign tumors were correctly identified. Conclusions: Washout CT with the established thresholds for APW und RPW is insufficient to reliably diagnose adrenal masses. Using the proposed cutoff of 58% for RPW, malignant tumors will be correctly identified, but the added value is limited, namely 15% of patients with benign tumors can be prevented from additional imaging or even unnecessary surgery.

Development and External Validation of the 3D U-Net Algorithm for Segmentation of Pelvic Lymph Nodes On Diffusion-Weighted Images

Background: The 3D U-Net model has been proved to perform well in the automatic organ segmentation. The aim of this study is to evaluate the feasibility of the 3D U-Net algorithm for the automated detection and segmentation of lymph nodes (LNs) on pelvic diffusion-weighted imaging (DWI) images.Methods: A total of 393 DWI images of patients suspected of having prostate cancer (PCa) between January 2019 and December 2020 were collected for model development. Seventy-seven DWI images from another group of PCa patients imaged between January 2021 and April 2021 were collected for external validation. Segmentation performance was assessed using the Dice score, positive predictive value (PPV), true positive rate (TPR), and volumetric similarity (VS) with manual annotation of pelvic LNs as the reference. The accuracy with which the suspicious metastatic LNs (short diameter > 0.8 cm) were detected was evaluated using the area under the curve (AUC) at the patient level, and the precision, recall, and F1-score were determined at the lesion level. The consistency of LN staging on an external dataset between the model and radiologist was assessed using Cohen’s kappa coefficient.Results: In the testing set used for model development, the Dice score, TPR, PPV, and VS for the segmentation of suspicious LNs were 0.85, 0.82, 0.80 and 0.86, respectively. The precision, recall, and F1-score for the detection of suspicious LNs were 0.97, 0.98 and 0.97, respectively. In the external validation dataset, the AUC of the model for identifying PCa patients with suspicious LNs was 0.963 (95% CI: 0.892-0.993). High consistency of LN staging (Kappa = 0.922) was achieved between the model and expert radiologist.Conclusion: The 3D U-Net algorithm can accurately detect and segment pelvic LNs based on DWI images.

Rhinoplasty Pre-Surgery Models by Using Low-Dose Computed Tomography, Magnetic Resonance Imaging, and 3D Printing

Rhinoplasty and surgical reconstruction of cartilaginous structures still remain a great challenge today. This study aims to identify an imaging strategy in order to merge the information from CT scans and magnetic resonance imaging (MRI) acquisitions and build a 3D printed model true to the patient’s anatomy, for better surgical planning. Using MRI, information can be obtained about the cartilage structures of which the nose is composed. Ten rhinoplasty candidate patients underwent both a low-dose protocol CT scan and a specific MRI for characterization of nasal structures. Bone and soft tissue segmentations were performed in CT, while cartilage segmentations were extrapolated from MRI and validated by both an expert radiologist and surgeon. Subsequently, a 3D model was produced in materials and colors reproducing the density of the three main structures (bone, soft tissue, and cartilage), useful for pre-surgical evaluation. This study has highlighted that the optimization of a CT and MR dedicated protocol has allowed to reduce the CT radiation dose up to 60% compared to standard acquisitions with the same machine, and MR acquisition time of about 20%. Patient-tailored 3D models and pre-surgical planning have reduced the mean operative time by 20 minutes.

NEIM-01. PREDICTION OF RESPONSE TO COMBINATION OF NIVOLUMAB AND BEVACIZUMAB IN PATIENTS WITH RECURRENT GLIOBLASTOMA VIA RADIOMIC ANALYSIS ON CLINICAL MRI SCANS

PURPOSE The use of immunotherapy in glioblastoma management is under active investigation. Glioblastomas are “cold” tumors, meaning that they have inactivated or fewer tumor infiltrative lymphocytes in addition to substantial tumor necrosis, attributing to their poor response to immunotherapy. A significant challenge is the apriori identification of Glioblastoma patients who will respond favorably to immunotherapy. In this work, we evaluated the ability of computerized MRI-based quantitative features (radiomics) extracted from the lesion habitat (including enhancing lesion, necrosis, and peritumoral hyperintensities) to predict response and progression-free survival (PFS) in recurrent GBM patients treated with combination of Nivolumab and Bevacizumab. METHODS Immunotherapy response assessment in neuro-oncology (iRANO) criteria along with PFS were used to analyze n=50 patients enrolled in a randomized clinical trial where patients received Nivolumab with either standard or low dose Bevacizumab. These patients were assessed to see if they had complete response, partial response, stable disease (i.e. responders, n=31), or disease progression (i.e. non-responders, n=19). Lesion habitat constituting necrotic core, enhancing tumor, and edema were delineated by expert radiologist on Gd-T1w, T2w and FLAIR MRI scans. COLIAGE radiomic features from each of the delineated regions were selected using minimum redundancy maximum relevance (mRMR) via cross-validation, to segregate non-responder patients from responders. A multivariable cox proportional hazard model was used to predict survival (PFS). RESULTS CoLlAGe correlation, sum average, and sum variance features (capture local heterogeneity) from the lesion habitat, were found to segregate non-responder patients from responders with an accuracy of 86%, followed by 80% using features from peritumoral hyperintensities and 78% from enhancing tumor. In our survival analysis, C-index of 0.688 was obtained using features from the entire lesion habitat, followed by peritumoral hyperintensities (0.675) and enhancing tumor (0.656). CONCLUSION Radiomic features from the lesion habitat may predict response to combination of Nivolumab and Bevacizumab in recurrent Glioblastomas.

A Novel Convolutional Neural Network for COVID-19 detection and classification using Chest X-Ray images

The early and rapid diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), the main cause of fatal pandemic coronavirus disease 2019 (COVID-19), with the analysis of patients chest X-ray (CXR) images has lifesaving importance for both patients and medical professionals. In this research a very simple novel and robust deep-learning convolutional neural network (CNN) model with less number of trainable-parameters is proposed to assist the radiologists and physicians in the early detection of COVID-19 patients. It also helps to classify patients into COVID-19, pneumonia and normal on the bases of analysis of augmented X-ray images. This augmented dataset contains 4803 COVID-19 from 686 publicly available chest X-ray images along with 5000 normal and 5000 pneumonia samples. These images are divided into 80% training and 20 % validation. The proposed CNN model is trained on training dataset and then tested on validation dataset. This model has a promising performance with a mean accuracy of 92.29%, precision of 99.96%, Specificity of 99.85% along with Sensitivity value of 85.92%. The result can further be improved if more data of expert radiologist is publically available.

Learning curves in radiological reporting of whole-body MRI in plasma cell disease: a retrospective study

Background The plasma cell disease is been studying by the whole-body MRI technology. However, the time requested to learn this radiological technique is unknown. Purpose To esteem, quantitatively and qualitatively, the essential time to learn the whole-body MRI diffusion-weighted imaging with background body signal suppression in patients with plasma cell disease. Materials and methods Between January 2015 and February 2017, three readers in-training with different levels of experience examined the anonymised and randomised whole-body MRI images of 52 patients with a diagnosis of plasma cell disease and analysed their morphological (T1w, T2w with and without fat suppression) and functional sequences. Reports of an expert radiologist were considered the standard of reference. Images were analysed in two sessions, during which each reader was timed. Readers reported the number of segments with lesions and staged the disease using the Durie–Salmon PLUS staging system. Weighted Cohen’s ĸ and Z-test were used to compare the trainees’ reports with those of the expert radiologist, and learning curves were drawn up to show changes between the two sessions. Results Weighted Cohen’s ĸ of number of lesioned segments increased from 0.536 ± 0.123 to 0.831 ± 0.129 (Prob > Z under 0.005), thus approaching the goal of ĸ > 0.8. Trainees reached the level of experienced radiologist in terms of time by the 33rd patient. Agreement concerning the Durie–Salmon PLUS increased from 0.536 ± 0.123 to 0.831 ± 0.129 (Prob > Z under 0.005). Conclusions The findings of this study demonstrate that whole-body MRI with DWIBS can be learned in about 80 reports and leads to a high level of inter-observer concordance when using the Durie–Salmon PLUS staging system.