Publications by authors named "Anurima Patra"

5 Publications

  • Page 1 of 1

Radiology Reporting Errors: Learning from Report Addenda.

Indian J Radiol Imaging 2021 Apr 12;31(2):333-344. Epub 2021 Aug 12.

Department of Radiology, Christian Medical College, Vellore, Tamil Nadu, India.

The addition of new information to a completed radiology report in the form of an "addendum" conveys a variety of information, ranging from less significant typographical errors to serious omissions and misinterpretations. Understanding the reasons for errors and their clinical implications will lead to better clinical governance and radiology practice. This article assesses the common reasons which lead to addenda generation to completed reports and their clinical implications. Retrospective study was conducted by reviewing addenda to computed tomography (CT), ultrasound, and magnetic resonance imaging reports between January 2018 to June 2018, to note the frequency and classification of report addenda. Rate of addenda generation was 1.1% ( = 1,076) among the 97,003 approved cross-sectional radiology reports. Errors contributed to 71.2% ( = 767) of addenda, most commonly communication (29.3%, = 316) and observational errors (20.8%, = 224), and 28.7% were nonerrors aimed at providing additional clinically relevant information. Majority of the addenda (82.3%, = 886) did not have a significant clinical impact. CT and ultrasound reports accounted for 36.9% ( = 398) and 35.2% ( = 379) share, respectively. A time gap of 1 to 7 days was noted for 46.8% ( = 504) addenda and 37.6% ( = 405) were issued in less than a day. Radiologists with more than 6-year experience created majority (1.5%, = 456) of addenda. Those which were added to reports generated during emergency hours contributed to 23.2% ( = 250) of the addenda. The study has identified the prevalence of report addenda in a radiology practice involving picture archiving and communication system in a tertiary care center in India. The etiology included both errors and non-errors. Results of this audit were used to generate a checklist and put protocols that will help decrease serious radiology misses and common errors.
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http://dx.doi.org/10.1055/s-0041-1734351DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8448237PMC
April 2021

Atypical Metastases in the Abdomen and Pelvis From Biochemically Recurrent Prostate Cancer: C-Choline PET/CT Imaging With Multimodality Correlation.

AJR Am J Roentgenol 2021 Aug 4. Epub 2021 Aug 4.

Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.

PET imaging with targeted radiotracers has become integral for mapping the location and burden of recurrent disease in patients with biochemical recurrence (BCR) of prostate cancer (PCa). PET with 11C-choline is part of the National Comprehensive Cancer Network and European Association of Urology guidelines for evaluation of BCR. With advances in PET technology, increasing use of targeted radiotracers, and improved survival of patients with BCR due to novel therapeutics, atypical sites of metastases are being increasingly encountered, challenging the conventional view that PCa rarely metastasizes beyond bones or lymph nodes. We describe such atypical metastases in the abdomen and pelvis on 11C-choline PET (including in the liver, pancreas, genital tract, urinary tract, peritoneum, and abdominal wall, as well as perineural spread), presenting multimodality imaging features and relevant imaging pitfalls. Given atypical metastases' inconsistent relationship with serum PSA and non-specific presenting symptoms, they are often first detected on imaging. Awareness of their imaging features is important as their detection impacts clinical management, patient counseling, prognosis, and clinical trial eligibility. Such awareness is particularly critical as the role of radiologists in the imaging and management of BCR will continue to increase given the expanding regulatory approvals of other targeted and theranostic radiotracers.
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http://dx.doi.org/10.2214/AJR.21.26426DOI Listing
August 2021

Diffuse Infiltrative Non-mass-like Brain Parenchymal Lesions on MRI: Differentiating Lymphomatosis Cerebri from its Mimics.

J Clin Imaging Sci 2021 23;11:41. Epub 2021 Jul 23.

Department of Radiology, Christian Medical College and Hospital, Vellore, Tamil Nadu, India.

Objectives: Diffuse infiltrative "non-mass-like" parenchymal lesions on MRI brain are a known presentation of an aggressive condition called lymphomatosis cerebri (LC) but are often misdiagnosed due to its non-specific clinical and imaging findings. We aim to identify clues to differentiate lymphomatosis from its less aggressive mimics based on imaging features.

Material And Methods: MRI brain studies showing diffuse infiltrative "non-mass-like" parenchymal lesions between January 2013 and March 2020 were retrospectively identified and read for lesion location, signal characteristics, and enhancement pattern by two radiologists. Additional findings on MRI spine and whole-body fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET-CT) were recorded wherever available. The clinical diagnosis, patient demographics, symptoms, laboratory and histopathology results, treatment details, and follow-up details were also noted.

Results: Of the 67 patients, 28 (41.7%) were diagnosed with lymphomatosis. The remaining 39 (13.4%) patients were classified as non-lymphomas (infective, vasculitis, and inflammatory conditions). Diffusion restriction on MRI (20/67, = 0.007) and increased regional activity on FDG PET-CT (12/31, = 0.017) were the two imaging parameters found to significantly favor lymphomatosis over other conditions, whereas the presence of microhemorrhages on susceptibility-weighted imaging was significantly associated with vasculitis ( = 0.002). Rapid clinical or imaging deterioration on a short trial of steroids ( = 0.00) was the only relevant clinical factor to raise an early alarm of lymphomatosis. Positive serological markers and non-central nervous system systemic diseases were associated with non-lymphomatous diseases.

Conclusion: LC and its less aggressive mimics can be differentiated on diffusion-weighted imaging-MRI and PET-CT when read in conjunction with rapid progression of clinical features, serological workup, and systemic evaluation.
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http://dx.doi.org/10.25259/JCIS_75_2021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8326078PMC
July 2021

Quality gaps in public pancreas imaging datasets: Implications & challenges for AI applications.

Pancreatology 2021 Aug 2;21(5):1001-1008. Epub 2021 Apr 2.

Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA. Electronic address:

Objective: Quality gaps in medical imaging datasets lead to profound errors in experiments. Our objective was to characterize such quality gaps in public pancreas imaging datasets (PPIDs), to evaluate their impact on previously published studies, and to provide post-hoc labels and segmentations as a value-add for these PPIDs.

Methods: We scored the available PPIDs on the medical imaging data readiness (MIDaR) scale, and evaluated for associated metadata, image quality, acquisition phase, etiology of pancreas lesion, sources of confounders, and biases. Studies utilizing these PPIDs were evaluated for awareness of and any impact of quality gaps on their results. Volumetric pancreatic adenocarcinoma (PDA) segmentations were performed for non-annotated CTs by a junior radiologist (R1) and reviewed by a senior radiologist (R3).

Results: We found three PPIDs with 560 CTs and six MRIs. NIH dataset of normal pancreas CTs (PCT) (n = 80 CTs) had optimal image quality and met MIDaR A criteria but parts of pancreas have been excluded in the provided segmentations. TCIA-PDA (n = 60 CTs; 6 MRIs) and MSD(n = 420 CTs) datasets categorized to MIDaR B due to incomplete annotations, limited metadata, and insufficient documentation. Substantial proportion of CTs from TCIA-PDA and MSD datasets were found unsuitable for AI due to biliary stents [TCIA-PDA:10 (17%); MSD:112 (27%)] or other factors (non-portal venous phase, suboptimal image quality, non-PDA etiology, or post-treatment status) [TCIA-PDA:5 (8.5%); MSD:156 (37.1%)]. These quality gaps were not accounted for in any of the 25 studies that have used these PPIDs (NIH-PCT:20; MSD:1; both: 4). PDA segmentations were done by R1 in 91 eligible CTs (TCIA-PDA:42; MSD:49). Of these, corrections were made by R3 in 16 CTs (18%) (TCIA-PDA:4; MSD:12) [mean (standard deviation) Dice: 0.72(0.21) and 0.63(0.23) respectively].

Conclusion: Substantial quality gaps, sources of bias, and high proportion of CTs unsuitable for AI characterize the available limited PPIDs. Published studies on these PPIDs do not account for these quality gaps. We complement these PPIDs through post-hoc labels and segmentations for public release on the TCIA portal. Collaborative efforts leading to large, well-curated PPIDs supported by adequate documentation are critically needed to translate the promise of AI to clinical practice.
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http://dx.doi.org/10.1016/j.pan.2021.03.016DOI Listing
August 2021

MR Imaging in Neurocritical Care.

Indian J Crit Care Med 2019 Jun;23(Suppl 2):S104-S114

Department of Radiodiagnosis, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India.

Patra A, Janu A, Sahu A. MR Imaging in Neurocritical Care. Indian J Crit Care Med 2019;23(Suppl 2):S104-S114.
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http://dx.doi.org/10.5005/jp-journals-10071-23186DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6707490PMC
June 2019
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