A Systematic Meta-Analysis of CT Features of COVID-19: Lessons from Radiology

Vasantha Kumar Venugopal, Vidur Mahajan, Sriram Rajan, Vikash Kumar Agarwal, Ruchika Rajan, Salsabeel Syed, Harsh Mahajan

This article is a preprint and has not been peer-reviewed [what does this mean?]. It reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice.


Several studies have been published in the past few months describing the CT features of Coronavirus Disease 2019 (COVID-19). There is a great degree of heterogeneity in the study designs, lesion descriptors used and conclusions derived. In our systematic analysis and meta-review, we have attempted to homogenize the reported features and provide a comprehensive view of the disease pattern and progression in different clinical stages. After an extensive literature search, we short-listed and reviewed 49 studies including over 4145 patients with 3615 RT-PCR positive cases of COVID-19 disease. We have found that there is a good agreement among these studies that diffuse bilateral ground-glass opacities (GGOs) is the most common finding at all stages of the disease followed by consolidations and mixed density lesions. 78% of patients with RT-PCR confirmed COVID-19 infections had either ground-glass opacities, consolidation or both. Inter-lobular septal thickening was also found to be a common feature in many patients in advanced stages. The progression of these initial patchy ground-glass opacities and consolidations to diffuse lesions with septal thickening, air bronchograms in the advanced stages, to either diffuse white-out lungs needing ICU admissions or finally resolving completely without or with residual fibrotic strips was also found to be congruent among multiple studies. Prominent juxta-lesional pulmonary vessels, pleural effusion and lymphadenopathy in RT-PCR proven cases were found to have poor clinical prognosis. Additionally, we noted wide variation in terminology used to describe lesions across studies and suggest the use of standardized lexicons to describe findings related to diseases of vital importance.

For Full Paper: https://www.medrxiv.org/content/10.1101/2020.04.04.20052241v1

A Comparison of the Reliability of the Patellar Tendon- Trochlear Groove (PTTG) Distance and the Tibial Tuberosity- Trochlear Groove (TTTG) Distance Measured on MRI


An increased tibial tuberosity-trochlear groove (TTTG) distance is used for deciding a treatment plan in patello-femoral instability (PFI). The centre of the patellar tendon and the chondral trochlear groove can be directly visualised on MRI, and measured, giving the patellar tendontrochlear groove (PTTG) distance. A study was designed to compare the inter-rater and the test-retest reliabilities of PTTG and TTTG measurements in MRI of patients without PFI and in a group with PFI.

Materials and Methods:

This cross-sectional reliability study was done on archival MRI films of 50 patients without patellar instability and 20 patients with patellar instability. TTTG and PTTG distances were independently measured by two orthopaedic surgeons and two radiologists. A hybrid PTTG measurement with bony landmarks on the femoral side and the patellar tendon landmark on the tibial side, was used to estimate the influence of the differences in the femoral and tibial landmarks on the difference in reliabilities. The intra-class correlation coefficient (ICC) was calculated for all four raters, as well as separately for each rater.


The PTTG distance had a higher inter-rater reliability (ICC=0.86, 95% CI=0.79-0.92) compared to the TTTG distance (ICC=0.70, 95% CI=0.59-0.80) in patients without PFI. Similar trends were seen in patients with PFI (0.83 vs 0.66). The inter-rater reliability for the hybrid PTTG distance was found to lie in between the TTTG and PTTG.


The MRI-based PTTG distance had better inter-rater reliability compared with the MRI-based TTTG distance.

Modified radial-search algorithm for segmentation of tibiofemoral cartilage in MR images of patients with subchondral lesion

Modified Radial-Search Algorithm For Segmentation Of Tibiofemoral Cartilage In MR Images Of Patients With Subchondral Lesion



The quantitative analysis of weight-bearing articular cartilage superficial to subchondral abnormality is important in osteoarthritis (OA) progression studies. The current study aimed to address the challenges of a semi-automatic segmentation of tibiofemoral cartilage in MR images of OA patient with and without subchondral bone abnormalities (SBA).


In this study, knee MRI data [fat-suppressed proton density-weighted, multi-echo T2-weighted (CartiGram) images] of 29 OA patients, acquired at 3.0T MR scanner, were retrospectively collected. Out of 29 data, 9 had SBA in femur bone. Initially, a semi-automatic femur cartilage segmentation based on radial intensity search approach by Akhtar et al. was implemented in-house. This algorithm was considered as the radial-search method for further comparison. In this current study, the reported radial-search (RS)-based semi-automatic cartilage segmentation method was modified using thresholding, connected component labelling, convex-hull operation and spline-based curve fitting for the improved segmentation of tibiofemoral cartilage. Cartilage was manually segmented by two experienced radiologists, and inter-reader variability was estimated using coefficient of variation (CV). The segmentation results were validated using dice coefficient (DC), Jaccard coefficient (JC) and sensitivity index measurements.


DC values for segmented femur cartilage in patients with SBA were 64.6 ± 7.8% and 81.4 ± 2.8% using reported RS method and modified radial-search method, respectively. DC values for segmented femur cartilage in patients without SBA were 82.5 ± 4.5% and 84.8 ± 2.0% using RS method and modified radial method, respectively. Similarly, DC values for tibial cartilage in all OA patients were 80.4 ± 1.6% and 81.9 ± 2.4% using RS method and modified radial method, respectively. Similar segmentation results were also obtained from the T2-weighted images. Inter-reader variability result based on CV in femur cartilage was 3.40 ± 2.12% (without SBA) and 4.18 ± 3.18% (with SBA).


In the current study, a semi-automated segmentation of tibiofemoral cartilage was presented. Modified radial-search approach can successfully segment tibiofemoral cartilage, and the results were tested and validated on knee MRI data of OA patients with and without SBA.

For full paper: http://https://link.springer.com/article/10.1007/s11548-020-02116-z

Metabolic imaging patterns in posterior cortical atrophy and Lewy body dementia


To study the imaging patterns of Posterior cortical atrophy (PCA) and Dementia with Lewy bodies (DLB) on fluoro-deoxyglucose positron emission tomography computed tomography ([18F]FDG PET/CT), identify areas of overlap and differences and to develop a prediction model to assist in diagnosis using univariate and multivariate analysis.


A retrospective analysis of 72 patients clinically suspected of having posterior dementia was done. All patients underwent [18FF]FDG PET/CT of the brain and dopamine transporter imaging with [[99mTc] TRODAT-1 SPECT scan on separate days. The patients were divided into PCA with normal TRODAT uptake (n=34) and DLB with abnormal TRODAT uptake (n=38). The FDG PET/CT uptake patterns were recorded and areas of significant hypometabolism by z score analysis were considered as abnormal. Receiver operator characteristics (ROC) curve analysis was used to determine cutoff z scores and binary logistic regression analysis was used to determine the Odds ratio of being in the predicted groups.


Significantly hypometabolism was found in parieto-temporo-occipital association cortices and cingulate cortices in PCA patients. DLB patients showed significantly reduced uptake in the visual cortex. No significant difference was found between z score of occipital association cortex which showed hypometabolism in both groups. The cut-off z-score values derived from the ROC curve analysis were as follows- parietal association (cut-off-3, sensitivity-65.6%, specificity – 68.7%), temporal association (cut-off-2, sensitivity-78%, specificity-75%) and posterior cingulate (cut-off-0.5, sensitivity-93.7%, specificity-40.6%), their respective Odds ratio (with 95% confidence interval) for being in the PCA group as derived from univariate logistic regression were 3.66 (1.30–10.32), 10.71 (3.36–34.13) and 7.85 (1.57–39.17). The cut-off z score of primary visual cortex as derived from ROC curve was zero with sensitivity of 87.5%, specificity of 71.9%, and the Odds ratio for being the in the DLB group was 24.7 with 95% confidence interval of 5.99–101.85.


[18F] FDG PET may be useful as a noninvasive diagnostic modality in differentiating the two posterior cortical dementias, despite significant overlap. Primary visual cortical hypometabolism can serve as
an independent diagnostic marker for DLB, even in the absence of TRODAT imaging.

Unboxing AI – Radiological Insights Into a Deep Neural Network for Lung Nodule Characterization

Rationale and Objectives: To explain predictions of a deep residual convolutional network for characterization of lung nodule by analyzing heat maps Materials and Methods A 20-layer deep residual CNN was trained on 1245 Chest CTs from NLST trial to predict the malignancy risk of a nodule. We used occlusion to systematically block regions of a nodule and map drops in malignancy risk score to generate clinical attribution heatmaps on 103 nodules from LIDC-IDRI dataset, which were analyzed by a thoracic radiologist. The features were described as heat inside nodule (IH)-bright areas inside nodule, peripheral heat (PH)-continuous/interrupted bright areas along nodule contours, heat in adjacent plane (AH)-brightness in scan planes juxtaposed with the nodule, satellite heat (SH)- a smaller bright spot in proximity to nodule in the same scan plane, heat map larger than nodule (LH)-bright areas corresponding to the shape of the nodule seen outside the nodule margins and heat in calcification (CH) Results These six features were assigned binary values. This feature vector was fed into a standard J48 decision tree with 10-fold cross-validation, which gave an 85 % weighted classification accuracy with a 77.8 %TP rate, 8% FP rate for benign cases and 91.8% TP and 22.2 %FP rates for malignant cases. IH was more frequently observed in nodules classified as malignant whereas PH, AH, and SH were more commonly seen in nodules classified as benign. Conclusion We discuss the potential ability of a radiologist to visually parse the deep learning algorithm-generated \’heat map\’ to identify features aiding classification

For full paper: http://vixra.org/abs/1909.0398?ref=10792316

The Algorithmic Audit: Working with Vendors to Validate Radiology-AI Algorithms – How We Do It


There is a plethora of Artificial Intelligence (AI) tools that are being developed around the world aiming at either speeding up or improving the accuracy of radiologists. It is essential for radiologists to work with the developers of such algorithms to determine true clinical utility and risks associated with these algorithms. We present a framework, called an Algorithmic Audit, for working with the developers of such algorithms to test and improve the performance of the algorithms. The framework includes concepts of true independent validation on data that the algorithm has not seen before, curating datasets for such testing, deep examination of false positives and false negatives (to examine implications of such errors) and real-world deployment and testing of algorithms.

Link – http://vixra.org/abs/1909.0104

Can AI Generate Clinically Appropriate X-Ray Reports? Judging the Accuracy and Clinical Validity of Deep Learning-generated Test Reports as Compared to Reports Generated by Radiologists: A Retrospective Comparative Study

Can AI Generate Clinically Appropriate X-Ray Reports? Judging The Accuracy And Clinical Validity Of Deep Learning-Generated Test Reports As Compared To Reports Generated By Radiologists: A Retrospective Comparative Study


Implementations of deep learning algorithms in clinical practice are limited by the nature of output provided by the algorithms. We evaluate the accuracy, clinical validity, clarity, consistency and level of hedging of AI-generated Chest X-Ray (CXRs) compared to radiologist-generated clinical reports.


297 CXRs done on a Conventional X-Ray system (GE Healthcare, USA) fitted with a Retrofit DR (Konika Minolta, Japan) were pulled from the PACS along with their corresponding reports. The anonymised CXRs were analysed by a CE approved deep learning-based CXR analysis algorithm (ChestEye, Oxipit, Lithuania) which detects abnormalities and autogenerates clinical reports. The algorithm is an ensemble of multiple classification, detection and segmentation neural networks capable of identifying 75 different radiological findings and perform findings\’ location extraction. The outputs from this model are used by a custom automatic text generator tailored by multiple radiologists to produce a structured and cohesive report. These models were trained using around 1 million chest X-rays coming from multiple data sources. The algorithm was not trained or tested before on CXRs from our institution. An informed review was performed by a radiologist with 9 years\’ experience to evaluate both the reports for the accuracy as well the clinical appropriateness of the reports.


In 236 (79%) cases, algorithm-generated reports were found to be as accurate as the radiologists\’ reports. In 16 (5%) cases, algorithm-generated reports were found to be either more accurate or more clinically appropriate. In 18 (6%) cases, the algorithm made significant diagnostic errors and in 27 (9%) cases, the algorithm-generated reports were found to be clinically inappropriate or insufficient even though the significant findings were correctly identified and localised.


We demonstrate, for the first time as of this date, a comparison between reports auto-generated by a deep learning algorithm and a practicing radiologist. We report good comparability of the clinical appropriateness of the reports generated by a DL network having high accuracy, paving the way for a new potential deployment strategy of AI in radiology.


We report on an algorithm with potential to produce standardized, accurate reports in a manner that is easily understandable and deployable in the clinical environment.

Spectrum of Autoimmune Limbic Encephalitis on FDG PET/CT

Spectrum Of Autoimmune Limbic Encephalitis On FDG PET/CT


To evaluate the role of FDG PET CT in the diagnosis, treatment response evaluation and follow up of patients with suspected autoimmune limbic encephalitis and correlation with specific antibody sub-type.


A retrospective analysis of 27 patients of clinically suspected and serologically proven cases of autoimmune encephalitis, who underwent FDG PET CT, was done. Whole body FDG PET CT scans were done in all the patients with separate special brain sequence. The patterns of FDG uptake in different antibody subtypes were recorded and comparison with normalized data was attempted. The areas of hypo/hypermetabolism that were two standard deviations from the mean were considered as abnormal. The patients were also analyzed based on the Z score surface maps of the 3D stereotactic surface projections (SSP) image and regional Z scores were evaluated. Post treatment follow-up scans were also acquired and analyzed.


Focal areas of hypermetabolism involving medial temporal regions, basal ganglia and thalami with relative global hypometabolism in rest of the cortical and subcortical structures was seen, both on visual inspection and on semiquantitative analysis. Serologically, 17 patients had antibodies against Voltage gated potassium channel (VGKC) complex /LGI1 receptors, 2 had antibodies against CRMP-5 (Anti-CV-2) and 1 had had antibodies against PCA-1/Anti-Yo receptor. We could not isolate the antibody in 7 patients. Suspicious mitotic lesions were identified in 10 patients on the whole body scan, which later were biopsied and characterized. No scan evidence of mitotic pathology was identified in 17 patients, thus were labeled as non-paraneoplastic.Depending on the temporal phase of the disease, focal hypermetabolism was found to be a feature of acute phase, whereas hypometabolic areas were seen in sub-acute and chronic phases of the disease. On follow-up, the post-treatment FDG PET CT scans obtained in some of these patients showed reversal to normal metabolism in the corresponding areas.


FDG PET/CT may have an important role both in the identification of Autoimmune encephalitis and in the detection of the unknown malignancy that might have caused it.


FDG PET CT scan is a non-invasive diagnostic modality in the early diagnosis and management of patients with clinical suspicion of autoimmune encephalitis

Next Generation Sequencing for the Practicing Radiologist

Next Generation Sequencing For The Practicing Radiologist


Next Generation Sequencing (NGS) is a technique of reading the entire genome or screening few relevant genes for aberrations. NGS sequences DNA parallelly thus resulting in reduced price and high speed of genome sequencing.

For Onco-Radiologists: NGS, can detect inherited or sporadic germline mutations or acquired changes i.e. somatic mutations on tumour tissues. Identification of germline mutations helps in confirming the diagnosis better management of diseases, prognosis and suggest preventive and surveillance measures for at risk unaffected relatives. Few radiological outcomes are spot diagnosis for a few cancer genetics syndrome. Somatic analysis of tumour biopsy helps in predicting the response of targeted therapy, prognostication and pharmacogenomics. For Ultrasonologists – For prenatal cases with abnormal USG findings, Non – Invasive Prenatal Techniques (NIPT), is a test that detects small amounts of cell free fetal DNA in the maternal bloodstream, allowing prenatal genetic diagnosis through maternal blood.


What is next generation sequencing?

Germline vs somatic mutation detection

Germline – applications in clinical care

Somatic mutations – large gene panels – what is their use?

NIPT – how can radiologists use it Imaging + genomics – the future?

Circulating Tumor DNA (ctDNA) – A Potential Adjunct to FDG-PET Imaging in Cancer Follow Up

Circulating Tumor DNA (CtDNA) – A Potential Adjunct To FDG-PET Imaging In Cancer Follow Up


• With the continuous advances in healthcare – Next Generation Sequencing and PET-CT are revolutionising cancer diagnostics

• Survival outcomes in cancer can be improved by early detection of metastatic disease or relapse during follow up

• Next Generation sequencing is an extremely high end modality wherein hundreds to millions of DNA molecules are sequenced in parallel

• CtDNA is an application of NGS to monitor the amount of tumor specific DNA in the peripheral blood of patient

• Using ctDNA, a non-invasive investigation, to monitor the remission status can potentially reduce the frequency of imaging required at follow up, hence reducing the side effects and discomfort

• According to Wong R et al, for colorectal cancer patients with indeterminate findings on routine investigations, ctDNA detection increases the probability that the findings indicate metastatic disease, including in a nonpredefined subset that also underwent FDG-PET imaging

• Hence, using ctDNA as an adjunct to FDG-PET imaging during cancer follow-up seems to be an acceptable proposition.


• What is ctDNA? How is it measured?

• What are the current guidelines for cancer follow up?

• Review of literature • Advantages and limitations of FDG-PET imaging

• Advantages and limitations of ctDNA

• Conclusion