Faculty Bibliography
Searched for:
person:jainr04
Total Results:
241
Periodic Alternating Gaze Deviation
PMID: 35421037
ISSN: 1536-5166
CID: 5204432
A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features
Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these complex models can be difficult to interpret and are often criticized as "black boxes". Prediction models that provide no insight into how their predictions are obtained are difficult to trust for making important clinical decisions, such as medical diagnoses or treatment. Explainable machine learning (XML) methods, such as Shapley values, have made it possible to explain the behavior of ML algorithms and to identify which predictors contribute most to a prediction. Incorporating XML methods into medical software tools has the potential to increase trust in ML-powered predictions and aid physicians in making medical decisions. Specifically, in the field of medical imaging analysis the most used methods for explaining deep learning-based model predictions are saliency maps that highlight important areas of an image. However, they do not provide a straightforward interpretation of which qualities of an image area are important. Here, we describe a novel pipeline for XML imaging that uses radiomics data and Shapley values as tools to explain outcome predictions from complex prediction models built with medical imaging with well-defined predictors. We present a visualization of XML imaging results in a clinician-focused dashboard that can be generalized to various settings. We demonstrate the use of this workflow for developing and explaining a prediction model using MRI data from glioma patients to predict a genetic mutation.
PMCID:9318445
PMID: 35890885
ISSN: 1424-8220
CID: 5276542
Federated Learning Enables Big Data for Rare Cancer Boundary Detection [PrePrint]
ORIGINAL:0015699
ISSN: 2331-8422
CID: 5284542
Neuroimaging of cerebrovascular complications in cancer patients
[S.l.] : Elsevier, 2022
pp. 935-954
ISBN: 9780128229958
CID: 5500792
Quantifying T2-FLAIR Mismatch Using Geographically Weighted Regression and Predicting Molecular Status in Lower-Grade Gliomas
BACKGROUND AND PURPOSE/OBJECTIVE:-mutant 1p/19q noncodeleted gliomas with a high positive predictive value. We have developed an approach to quantify the T2-FLAIR mismatch signature and use it to predict the molecular status of lower-grade gliomas. MATERIALS AND METHODS/METHODS:We used multiparametric MR imaging scans and segmentation labels of 108 preoperative lower-grade glioma tumors from The Cancer Imaging Archive. Clinical information and T2-FLAIR mismatch sign labels were obtained from supplementary material of relevant publications. We adopted an objective analytic approach to estimate this sign through a geographically weighted regression and used the residuals for each case to construct a probability density function (serving as a residual signature). These functions were then analyzed using an appropriate statistical framework. RESULTS:-mutant 1p/19q noncodeleted class of tumors versus other categories. Our classifier predicts these cases with area under the curve of 0.98 and high specificity and sensitivity. It also predicts the T2-FLAIR mismatch sign within these cases with an under the curve of 0.93. CONCLUSIONS:-mutation and 1p/19q codeletion status with high predictive power. The utility of the proposed quantification of the T2-FLAIR mismatch sign can be potentially validated through a prospective multi-institutional study.
PMID: 34764084
ISSN: 1936-959x
CID: 5050712
Causal Inference in Radiomics: Framework, Mechanisms, and Algorithms
The widespread use of machine learning algorithms in radiomics has led to a proliferation of flexible prognostic models for clinical outcomes. However, a limitation of these techniques is their black-box nature, which prevents the ability for increased mechanistic phenomenological understanding. In this article, we develop an inferential framework for estimating causal effects with radiomics data. A new challenge is that the exposure of interest is latent so that new estimation procedures are needed. We leverage a multivariate version of partial least squares for causal effect estimation. The methodology is illustrated with applications to two radiomics datasets, one in osteosarcoma and one in glioblastoma.
PMCID:9261933
PMID: 35812228
ISSN: 1662-4548
CID: 5279682
A Unified Approach to Analysis of MRI Radiomics of Glioma Using Minimum Spanning Trees
ISI:000797934200001
ISSN: 2296-424x
CID: 5284532
Prognostic indicators and outcomes of hospitalised COVID-19 patients with neurological disease: An individual patient data meta-analysis
BACKGROUND:Neurological COVID-19 disease has been reported widely, but published studies often lack information on neurological outcomes and prognostic risk factors. We aimed to describe the spectrum of neurological disease in hospitalised COVID-19 patients; characterise clinical outcomes; and investigate factors associated with a poor outcome. METHODS:We conducted an individual patient data (IPD) meta-analysis of hospitalised patients with neurological COVID-19 disease, using standard case definitions. We invited authors of studies from the first pandemic wave, plus clinicians in the Global COVID-Neuro Network with unpublished data, to contribute. We analysed features associated with poor outcome (moderate to severe disability or death, 3 to 6 on the modified Rankin Scale) using multivariable models. RESULTS:We included 83 studies (31 unpublished) providing IPD for 1979 patients with COVID-19 and acute new-onset neurological disease. Encephalopathy (978 [49%] patients) and cerebrovascular events (506 [26%]) were the most common diagnoses. Respiratory and systemic symptoms preceded neurological features in 93% of patients; one third developed neurological disease after hospital admission. A poor outcome was more common in patients with cerebrovascular events (76% [95% CI 67-82]), than encephalopathy (54% [42-65]). Intensive care use was high (38% [35-41]) overall, and also greater in the cerebrovascular patients. In the cerebrovascular, but not encephalopathic patients, risk factors for poor outcome included breathlessness on admission and elevated D-dimer. Overall, 30-day mortality was 30% [27-32]. The hazard of death was comparatively lower for patients in the WHO European region. INTERPRETATION:Neurological COVID-19 disease poses a considerable burden in terms of disease outcomes and use of hospital resources from prolonged intensive care and inpatient admission; preliminary data suggest these may differ according to WHO regions and country income levels. The different risk factors for encephalopathy and stroke suggest different disease mechanisms which may be amenable to intervention, especially in those who develop neurological symptoms after hospital admission.
PMCID:9162376
PMID: 35653330
ISSN: 1932-6203
CID: 5277632
Increase in Ventricle Size and the Evolution of White Matter Changes on Serial Imaging in Critically Ill Patients with COVID-19
BACKGROUND:Evolution of brain magnetic resonance imaging (MRI) findings in critically ill patients with coronavirus disease 2019 (COVID-19) is unknown. METHODS:We retrospectively reviewed 4530 critically ill patients with COVID-19 admitted to three tertiary care hospitals in New York City from March 1 to June 30, 2020 to identify patients who had more than one brain MRI. We reviewed the initial and final MRI for each patient to (1) measure the percent change in the bicaudate index and third ventricular diameter and (2) evaluate changes in the presence and severity of white matter changes. RESULTS:Twenty-one patients had two MRIs separated by a median of 22 [Interquartile range (IQR) 14-30] days. Ventricle size increased for 15 patients (71%) between scans [median bicaudate index 0.16 (IQR 0.126-0.181) initially and 0.167 (IQR 0.138-0.203) on final imaging (p < 0.001); median third ventricular diameter 6.9 mm (IQR 5.4-10.3) initially and 7.2 mm (IQR 6.4-10.8) on final imaging (p < 0.001)]. Every patient had white matter changes on the initial and final MRI; between images, they worsened for seven patients (33%) and improved for three (14%). CONCLUSIONS:On serial imaging of critically ill patients with COVID-19, ventricle size frequently increased over several weeks. White matter changes were often unchanged, but in some cases they worsened or improved, demonstrating there is likely a spectrum of pathophysiological processes responsible for these changes.
PMCID:7935478
PMID: 33674942
ISSN: 1556-0961
CID: 4823352
COVID-19 associated brain/spinal cord lesions and leptomeningeal enhancement: A meta-analysis of the relationship to CSF SARS-CoV-2
BACKGROUND AND PURPOSE/OBJECTIVE:We reviewed the literature to evaluate cerebrospinal fluid (CSF) results from patients with coronavirus disease 2019 (COVID-19) who had neurological symptoms and had an MRI that showed (1) central nervous system (CNS) hyperintense lesions not attributed to ischemia and/or (2) leptomeningeal enhancement. We sought to determine if these findings were associated with a positive CSF severe acute respiratory syndrome associated coronavirus 2 (SARS-CoV-2) polymerase chain reaction (PCR). METHODS:We performed a systematic review of Medline and Embase from December 1, 2019 to November 18, 2020. CSF results were evaluated based on the presence/absence of (1) ≥ 1 CNS hyperintense lesion and (2) leptomeningeal enhancement. RESULTS:In 117 publications, we identified 193 patients with COVID-19 who had an MRI of the CNS and CSF testing. There were 125 (65%) patients with CNS hyperintense lesions. Patients with CNS hyperintense lesions were significantly more likely to have a positive CSF SARS-CoV-2 PCR (10% [9/87] vs. 0% [0/43], p = 0.029). Of 75 patients who had a contrast MRI, there were 20 (27%) patients who had leptomeningeal enhancement. Patients with leptomeningeal enhancement were significantly more likely to have a positive CSF SARS-CoV-2 PCR (25% [4/16] vs. 5% [2/42], p = 0.024). CONCLUSION/CONCLUSIONS:The presence of CNS hyperintense lesions or leptomeningeal enhancement on neuroimaging from patients with COVID-19 is associated with increased likelihood of a positive CSF SARS-CoV-2 PCR. However, a positive CSF SARS-CoV-2 PCR is uncommon in patients with these neuroimaging findings, suggesting they are often related to other etiologies, such as inflammation, hypoxia, or ischemia.
PMID: 34105198
ISSN: 1552-6569
CID: 4900822
