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

Tips and Tricks on Basic Programming Tools for Radiologists to Handle DICOM Data

Tips And Tricks On Basic Programming Tools For Radiologists To Handle DICOM Data


• In the era of artificial intelligence, it is beneficial for radiologists to learn some basic programming tools to organise

and curate DICOM data.

• There are numerous user-friendly simple tools available, that can be used in their clinical and research practice.

• DCM4CHE and DCMTK are open-source tools that can be used for the following:

a. Extracting images from PACS systems, using filters like study date, modality etc.

b. Data sorting, Data modification and analysis.

c. Converting Dicom (DCM) images to Jpeg or Pdf.

d. Transferring Data to PACS or any other viewer.

• Python is one of the easier programming languages which a radiologist, without much programming background,

can learn and start using for manipulating DICOM data. Some python modules to use radiology are:

a. Pydicom

b. Matplotlib

c. Pynetdicom3

d. tqdm


• Why a radiologist should learn Basic programming tools.

• About DCM Toolkit & how to install

• Multiple Functions of DCM Toolkit & how to use them.

• What is Python? How to Install Python.

• What is pip? How to install python modules using pip.

• Use of Python to modify Dicom’s Metadata.

Building Robust ML Models Using Federated Learning: The Future of AI Deployment

Building Robust ML Models Using Federated Learning: The Future Of AI Deployment


What does Deep Neural Nets learn?

Are they cramming or they are learning?

How to avoid cramming and move towards learning.

How to measure learning metrics

Is it sufficient to learn once?

What about the kinds of data the model has not seen during training phase?

Is it possible to bring all the varieties of training in one place? Just like we can bring all the pictures of dog species at one place?

If a false diagnosis was found in one location, shouldn’t the global model learn from it?

What is federated learning?

How is federated learning done in practice?

What about the privacy of data?

Issues around ownership of the final model?

Existing frameworks (in beta versions): Tensorflow federated, Pytorch Pysift

Live example with one of the frameworks.


Understanding how the ML model was built? Where did the training data come from? What were the test metrics?

Understanding Overfitting (or generalisability) of a model

Active Learning in Medicine

Federated Learning

Sample code/ tutorial

How to Lie with Statistics: Things To Keep in Mind While Evaluating a Deep Learning Claim

How To Lie With Statistics: Things To Keep In Mind While Evaluating A Deep Learning Claim


1. In today\’s age of deep learning and artificial intelligence, a radiologist must know what to watch out for while

evaluating a deep learning algorithm\’s claim

2. What is ground truth?

3. Specific points to keep in mind while evaluating:

What is the medium of communication? Is it a video, a pre-print or a reputed peer-reviewed journal article?

What is the performance metric? Accuracy is a bad metric to use.

What data was the algorithm developed on? Generally, algorithms developed on poor ground truth have poor performance

What data was the algorithm validated on? Generally, algorithms validated on data from the same institution from where training data was obtained tend to falsely perform better

How much data was it tested on? Test data should not only be independent, but also adequate, both in number and disease heterogeneity.

What are the implications of the algorithm failing – what if a chest X-Ray algorithm misses a critical finding?

4. Try to get access to the actual algorithm and run it in your department


1. Why should a radiologist know how to evaluate a deep learning algorithm?

2. Performance metrics for evaluating algorithms

3. Data – training and testing

4. When an algorithm fails – implications

5. Run AI in your department!

Practical Guide for Deployment of AI Solutions in Clinical Environment: How Did We Do It?

Practical Guide For Deployment Of AI Solutions In Clinical Environment: How Did We Do It?


• Every AI company developing/validating algorithms on different modalities has this question in mind – how to deploy

AI algorithms in a radiology department?

• Different types of deployment options:

o On-cloud

o On-prem (CPU-only)

o On-prem (CPU + GPU)

• How to integrate deployment with the department\’s PACS/Workstation?

• Specific Site wise deployment because of Non Uniform data in same modality\’s images.

• How deploying AI algorithms in docker form will be a wise choice?

• IOT Devices like raspberry pi can be used for Image data anonymization in an On-cloud setup.

• How can the result of the algorithm be transferred back to PACS/Workstation?

• How can the result also be shown in the hospital or radiology centre’s HIS/RIS using HL7 messaging?

• What type of Hardware configuration can be used in an On-prem deployment?

• How to ensure Patient’s Data privacy?

Getting AI Ready for Deployment: Tuning Algorithms to Specific Sites Using a Single Chest X-Ray Image

Getting AI Ready For Deployment: Tuning Algorithms To Specific Sites Using A Single Chest X-Ray Image


Lack of generalisation of deep neural networks, due to equipment and geographic variability, is a known problem facing the radiology community today. We propose a novel method to get algorithms ‘deployment ready’ by using a single reference Chest X-Ray(CXR) image from a potential deployment site, with the intention of automatically reading all ‘normal’ CXRs.


A deep learning model based on DenseNet-121 (M1) was trained on ~250,000 CXRs from Chexpert Dataset and~50,000 CXRs from NIH CXR14 dataset to predict a ‘normal’ or ‘abnormal’ label. The model was evaluated on 3datasets – E1(n=3587), E2(n=200) and E3(n=212). E1 and E3 were 2 separate datasets obtained from 3 outpatient imaging centres and 3 hospital imaging departments. E2 is Chexpert validation dataset. M2, a Siamese variation of  M1 uses a reference image (to capture site / scanner specific variation) for every site and was evaluated on E1, E2 and E3. A comparison between specificity of M1 and M2 was done by choosing a definite sensitivity threshold (97%) to determine their capability to correctly identify normal CXRs.


Area Under Receiver Operator Curve (AUROC) increased from 0.92, 0.87 and 0.84 on M1 to 0.95, 0.89 and 0.89 for M2 for E1, E2 and E3 respectively. At 97% sensitivity, M1 had a specificity of 0.41, 0.29 and 0.02 on E1, E2 and E3 respectively, which, after tuning M1 with a single reference image (M2), increased to 0.63,0.29, 0.45.


Our results indicate that deep learning models can be generalised across equipment, institutions and countries b y simply using a single reference image to tune the functioning of the model, hence showing potential to improve the functioning of deep learning algorithms in general. In this case, we observe drastic improvement in results of a model that distinguishes normal from abnormal images with a high degree of confidence.


More than 50% of all CXRs done across the world are reported as ‘normal’. We demonstrate a novel method where a single algorithm can be deployed across sites to automate reading of normal CXRs while having high sensitivity saving radiologists’ time and improving speed of reporting.

Mediastinal Lymph Nodal Staging by 18 F FDG PET CT in Patients with Coexistent Carcinoma Lung and Tuberculosis: A Tertiary Care Centre Experience (RSNA 2019, Sun Dec 01 – 06)

Mediastinal Lymph Nodal Staging By 18 F FDG PET CT In Patients With Coexistent Carcinoma Lung And Tuberculosis: A Tertiary Care Centre Experience (RSNA 2019, Sun Dec 01 – 06)


The aim of this study is to evaluate the imaging characteristics of metastatic and benign (Tubercular) lymph nodes on18 F FDG PET/CT, in patients with co-existent Carcinoma lung and Tuberculosis, and correlation with histo-pathological analysis.


A retrospective analysis of 25 patients (19 males, 6 females; mean age 62.4+/- 10.08 years) with co-existent Carcinoma lung and Tuberculosis was done. All the subjects underwent F-18 FDG PET/CT scanning and subsequently the mediastinal lymph nodes were biopsied. SUV Max-Tumour, SUV Max-Lymph node and SUV Max-Ratio ( SUV Max Lymph node / SUV Max Tumour ) for each lymph node station on 18F-FDG PET/CT was determined and then each station was classified into one of the three groups based on SUV Max -Tumour (low, medium and high SUV Max -Tumour groups). Diagnostic performance was assessed based on receiver operating characteristic (ROC) curve analysis, and the optimal cut-off values that would best discriminate metastatic from benign lymph nodes were determined for each method.


A total of 115 lymph node stations with a mean of 4.6 lymph node station per patient and total of 540 lymph nodes with a mean of 21.6 lymph nodes per patient were resected and biopsied. 79 nodes were reported positive for metastasis and 27 nodes were reported as granulomatous. On pre-treatment 18F-FDG PET/CT scan, the mean SUV Max-Tumour of squamous cell carcinoma was significantly higher than that of adenocarcinoma (9.9±3.97 vs. 5.76±3.48, P<0.001). The mean SUV max of malignant lymph nodes was significantly higher than that of tubercular lymph nodes (6.7±0.94 vs. 2.7± 0.84 P<0.001). The mean SUV Max -Ratio in patients with malignant lymph nodes was significantly higher than in those with tubercular lymph nodes (0.91±0.36 vs. 0.41±0.28, P<0.001).


The overall diagnostic accuracy of 18 F FDG PET CT in mediastinal lymph nodal staging in patients with co-existent Tuberculosis and Carcinoma lung carcinoma is 67.4 %, if SUV Max of 2.5 is taken as the cut off criteria, however if SUV Max-Ratio is taken into consideration, the overall diagnostic accuracy increases to 74.8%, thus helping in the accurate staging of patients


Carcinoma lung with co-existing Tuberculosis results in false positive mediastinal lymph nodes and fallacies in preoperative staging.

Making Spine MR Reports More Clinically Appropriate: A Questionnaire-based Survey of Sub-specialty Spine Surgeons

Making Spine MR Reports More Clinically Appropriate: A Questionnaire-Based Survey Of Sub-Specialty Spine Surgeons


MR reports have been unchanged for a long time, and clinical relevance of MR findings are being challenged in literature. We assess the weightage that spine surgeons give to certain aspects of the MR report, their preference for report structure and towards different modalities.


An anonymous online survey, created in consultation with 5 spine surgeons, which included questions related measurement of spinal canal dimensions, information about nerve root impingement, anomalies and take-off, annular fissures, Modic changes, scoliosis and listhesis, was circulated amongst sub-specialist spine surgeons.

Preference for report format (every level reported, significant levels reported, pain chart diagram), and modality of investigation before surgery for lumbar degenerative disc disease was also recorded.


24 sub-specialist spine surgeons, with average 13.9 years’ experience (range: 3 – 30 years) from 6 cities, completed the questionnaire. Responses were weighted towards surgically relevant details such as effective spinal canal measurement (79%), nerve root impingement (91%), obvious anomalies at the level of significant disc (61%), level of nerve root take-off (75%), only details of posterior annular fissures (50%), and 25% surgeons preferred “hyperintense zone terminology”. Surprisingly, equal number of responses for Modic changes (62%), and for the possibility of inflammatory spondyloarthropathy (58%) or infection (67%) we obtained. On reporting formats, majority asked for only involved levels (71%) while 33% asked for every level. 33% asked for a diagrammatic pain chart. There was no consensus on reporting of scoliosis cases. Also, majority asked for information about cause of listhesis. As expected, for pre-surgical assessment for degenerative disc disease, MR (87%) with and X ray spine with flexion and extension (75%) was preferred while only 8.3% asked for plain CT and none asked for CT myelography.


These results highlight clinically relevant information that should be included on an MR report, including effective spinal canal dimensions, details of nerve root anomalies at the level of disc herniation, details of nerve root impingement. There was lack of consensus on Modic changes, format of report, and scoliosis assessment.


Two-way communication between spine surgeons, and radiologists helps in generation of effective reports, that improve clinical outcomes.