Non-invasive postmortems using CT scans are feasible, study finds

BMJ ◽  
2017 ◽  
pp. j2571
Author(s):  
Zosia Kmietowicz
Keyword(s):  
Ct Scans ◽  
BJS Open ◽  
2021 ◽  
Vol 5 (1) ◽  
Author(s):  
M J Wilkinson ◽  
H Snow ◽  
K Downey ◽  
K Thomas ◽  
A Riddell ◽  
...  

Abstract Background Diagnosis of lymph node (LN) metastasis in melanoma with non-invasive methods is challenging. The aim of this study was to evaluate the diagnostic accuracy of six LN characteristics on CT in detecting melanoma-positive ilioinguinal LN metastases, and to determine whether inguinal LN characteristics can predict pelvic LN involvement. Methods This was a single-centre retrospective study of patients with melanoma LN metastases at a tertiary cancer centre between 2008 and 2016. Patients who had preoperative contrast-enhanced CT assessment and ilioinguinal LN dissection were included. CT scans containing significant artefacts obscuring the pelvis were excluded. CT scans were reanalysed for six LN characteristics (extracapsular spread (ECS), minimum axis (MA), absence of fatty hilum (FH), asymmetrical cortical nodule (CAN), abnormal contrast enhancement (ACE) and rounded morphology (RM)) and compared with postoperative histopathological findings. Results A total of 90 patients were included. Median age was 58 (range 23–85) years. Eighty-eight patients (98 per cent) had pathology-positive inguinal disease and, of these, 45 (51 per cent) had concurrent pelvic disease. The most common CT characteristics found in pathology-positive inguinal LNs were MA greater than 10 mm (97 per cent), ACE (80 per cent), ECS (38 per cent) and absence of RM (38 per cent). In multivariable analysis, inguinal LN characteristics on CT indicative of pelvic disease were RM (odds ratio (OR) 3.3, 95 per cent c.i. 1.2 to 8.7) and ECS (OR 4.2, 1.6 to 11.3). Cloquet’s node is known to be a poor predictor of pelvic spread. Pelvic LN disease was present in 50 per cent patients, but only 7 per cent had a pathology-positive Cloquet’s node. Conclusion Additional CT radiological characteristics, especially ECS and RM, may improve diagnostic accuracy and aid clinical decisions regarding the need for inguinal or ilioinguinal dissection.


2019 ◽  
Vol 44 (4) ◽  
pp. 704-714 ◽  
Author(s):  
Rasmus Kirkeskov Carlsen ◽  
Simon Winther ◽  
Christian D. Peters ◽  
Esben Laugesen ◽  
Dinah S. Khatir ◽  
...  

Background: Central blood pressure (BP) assessed noninvasively considerably underestimates true invasively measured aortic BP in chronic kidney disease (CKD) patients. The difference between the estimated and the true aortic BP increases with decreasing estimated glomerular filtration rates (eGFR). The present study investigated whether aortic calcification affects noninvasive estimates of central BP. Methods: Twenty-four patients with CKD stage 4–5 undergoing coronary angiography and an aortic computed tomography scan were included (63% males, age [mean ± SD ] 53 ± 11 years, and eGFR 9 ± 5 mL/min/1.73 m2). Invasive aortic BP was measured through the angiography catheter, while non-invasive central BP was obtained using radial artery tonometry with a SphygmoCor® device. The Agatston calcium score (CS) in the aorta was quantified on CT scans using the CS on CT scans. Results: The invasive aortic systolic BP (SBP) was 152 ± 23 mm Hg, while the estimated central SBP was 133 ± 20 mm Hg. Ten patients had a CS of 0 in the aorta, while 14 patients had a CS >0 in the aorta. The estimated central SBP was lower than the invasive aortic SBP in patients with aortic calcification compared to patients without (mean difference 8 mm Hg, 95% CI 0.3–16; p = 0.04). The brachial SBP was lower than the aortic SBP in patients with aortic calcification compared to patients without (mean difference 10 mm Hg, 95% CI 2–19; p = 0.02). Conclusion: In patients with advanced CKD the presence of aortic calcification is associated with a higher difference between invasively measured central aortic BP and non-invasive estimates of central BP as compared to patients without calcifications.


Author(s):  
Fabrice Marquet ◽  
Mathieu Pernot ◽  
Jean-Francois Aubry ◽  
Gabriel Montaldo ◽  
Mickael Tanter ◽  
...  

2003 ◽  
Vol 51 (4) ◽  
pp. 485-491 ◽  
Author(s):  
T. Magyar ◽  
F. Kovács ◽  
T. Donkó ◽  
H. Bíró ◽  
R. Romvári ◽  
...  

Computed tomography (CT), a non-invasive visualisation technique was applied for imaging the bony structures of the nasal cavity of pigs, and compared to the traditional scoring system of turbinate atrophy in swine. Twenty-three 27-week-old pigs representing various stages of turbinate atrophy were used. Nasal structures were visually scored on CT scans and transversal cuts of the noses at the level of the first upper premolar teeth using the same scoring system in both cases. A tissue/air area ratio was also determined based on density differences. A highly significant correlation was found between visual scoring of CT images and transversal cuts of pig noses (r = 0.98, p < 0.0001) as well as between visual scoring of CT images and tissue/air area ratio determination (r = -0.82, p < 0.0001).


Author(s):  
Fabrice Marquet ◽  
Mathieu Pernot ◽  
Jean-Francois Aubry ◽  
Gabriel Montaldo ◽  
Mickael Tanter ◽  
...  

Kidney360 ◽  
2021 ◽  
pp. 10.34067/KID.0003662021
Author(s):  
Chanon Chantaduly ◽  
Hayden R. Troutt ◽  
Karla A. Perez Reyes ◽  
Jonathan E. Zuckerman ◽  
Peter D. Chang ◽  
...  

Background: The goal of the Artificial Intelligence in Renal Scarring (AIRS) study is to develop machine learning tools for non-invasive quantification of kidney fibrosis from imaging scans. Methods: We conducted a retrospective analysis of patients who had one or more abdominal computed tomography (CT) scans within 6 months of a kidney biopsy. The final cohort encompassed 152 CT scans from 92 patients which included images of 300 native kidneys and 76 transplant kidneys. Two different convolutional neural networks (slice-level and voxel-level classifiers) were tested to differentiate severe vs mild/moderate kidney fibrosis (≥50% vs <50%). Interstitial fibrosis and tubular atrophy scores from kidney biopsy reports were used as ground-truth. Results: The two machine learning models demonstrated similar positive predictive value (0.886 vs 0.935) and accuracy (0.831 vs 0.879). Conclusions: In summary, machine learning algorithms are a promising non-invasive diagnostic tool to quantify kidney fibrosis from CT scans. The clinical utility of these prediction tools, in terms of avoiding renal biopsy and associated bleeding risks in patients with severe fibrosis, remains to be validated in prospective clinical trials.


2017 ◽  
Vol 28 (1) ◽  
pp. 9-20 ◽  
Author(s):  
B. Kelly Han ◽  
Marnie Huntley ◽  
David Overman ◽  
Dawn Witt ◽  
David Dassenko ◽  
...  

AbstractObjectiveWe sought to evaluate the risk and image quality from cardiovascular CT in patients across all stages of single-ventricle palliation, and to define accuracy by comparing findings with intervention and surgery.MethodsConsecutive CT scans performed in patients with single-ventricle heart disease were retrospectively reviewed at a single institution. Diagnosis, sedation needs, estimated radiation dose, and adverse events were recorded. Anatomical findings, image quality (1–4, 1=optimal), and discrepancy compared with interventional findings were determined. Results are described as medians with their 25th and 75th percentiles.ResultsFrom January, 2010 to August, 2015, 132 CT scans were performed in single-ventricle patients of whom 20 were neonates, 52 were post-Norwood, 15 were post-Glenn, and 45 were post-Fontan. No sedation was used in 76 patients, 47 were under minimal or moderate sedation, and nine were under general anaesthesia. The median image quality score was 1.2. The procedural dose–length product was 24 mGy-cm, and unadjusted and adjusted radiation doses were 0.34 (0.2, 1.8) and 0.82 (0.55, 1.88) mSv, respectively. There was one adverse event. No major and two minor discrepancies were noted at the time of 79 surgical and 10 catheter-based interventions.ConclusionsCardiovascular CT can be performed with a low radiation exposure in patients with single-ventricle heart disease. Its accuracy compared with that of interventional findings is excellent. CT is an effective advanced imaging modality when a non-invasive pathway is desired, particularly if cardiac MRI poses a high risk or is contraindicated.


2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S613-S614
Author(s):  
Melissa Sanacore ◽  
Melhem M Solh ◽  
H Kent Holland ◽  
Asad Bashey ◽  
Samuel Webster ◽  
...  

Abstract Background Improving diagnostics have led to newly identified causes of invasive fungal infection (IFI) in immunocompromised hosts. Syncephalastrum spp. are Zygomycetes more commonly associated with skin infections and have only rarely been implicated as a cause of IFI(1). Next generation sequencing (NGS) for circulating microbial cell-free DNA (mcfDNA) in plasma offers a unique tool to diagnose rare causes of IFI (2,3). Methods Karius results were reviewed for Syncephalastrum detections with 2 identified at the same institution. McfDNA was extracted from plasma and NGS was performed by Karius, Inc. (Redwood City, California). Human sequences were removed and remaining sequences were aligned to a database of over 1,400 pathogens. Organisms present above a predefined statistical significance threshold were quantified in DNA molecules per microliter (MPM). Chart review was performed for clinical correlation. Results A 66 y/o male one month out of induction therapy for acute myeloblastic leukemia (AML) developed pneumonia. Although BAL was negative for mold and despite empiric antifungals, plasma NGS for mcfDNA showed S. monosporum at 562 MPM; the reference range is 0 MPM. Amphotericin was added to empiric posaconazole. The patient was discharged 10 days later and serial CT scans showed improvement. Repeat NGS mcfDNA 11 days later was negative. He underwent stem cell transplant (SCT) 4 months later. In a second case, a 66 y/o female with acute prolymphocytic leukemia was admitted for fever with neutropenia. A CT chest showed new multifocal, bilateral, nodular opacities. Despite negative BAL fungal culture and pretreatment with fluconazole, plasma NGS mcfDNA revealed S. monosporum at 575 MPM. She was treated with micafungin, amphotericin, and posaconazole with clinical improvement. Repeat NGS mcfDNA 8 weeks later was negative. Serial CT scans showed improvement over 5 months. She proceeded to SCT. Conclusion Plasma-based NGS for mcfDNA enabled rapid, non-invasive detection of pulmonary mucormycosis caused by S. monosporum despite antifungal pretreatment and unrevealing invasive procedures in 2 patients with leukemia. The rapid identification of the specific etiology of IFI enabled targeted anti-fungal therapy and resumption of definitive oncological care including SCT. Table 1: Clinical Parameters Disclosures Christiaan R. de Vries, MD, PhD, Karius (Consultant, Independent Contractor)Stanford University (Employee)


2019 ◽  
Vol 92 (1099) ◽  
pp. 20190159 ◽  
Author(s):  
Usman Bashir ◽  
Bhavin Kawa ◽  
Muhammad Siddique ◽  
Sze Mun Mak ◽  
Arjun Nair ◽  
...  

Objective: Non-invasive distinction between squamous cell carcinoma and adenocarcinoma subtypes of non-small-cell lung cancer (NSCLC) may be beneficial to patients unfit for invasive diagnostic procedures or when tissue is insufficient for diagnosis. The purpose of our study was to compare the performance of random forest algorithms utilizing CT radiomics and/or semantic features in classifying NSCLC. Methods: Two thoracic radiologists scored 11 semantic features on CT scans of 106 patients with NSCLC. A set of 115 radiomics features was extracted from the CT scans. Random forest models were developed from semantic (RM-sem), radiomics (RM-rad), and all features combined (RM-all). External validation of models was performed using an independent test data set (n = 100) of CT scans. Model performance was measured with out-of-bag error and area under curve (AUC), and compared using receiver-operating characteristics curve analysis on the test data set. Results: The median (interquartile-range) error rates of the models were: RF-sem 24.5 % (22.6 – 37.5 %), RF-rad 35.8 % (34.9 – 38.7 %), and RM-all 37.7 % (37.7 – 37.7). On training data, both RF-rad and RF-all gave perfect discrimination (AUC = 1), which was significantly higher than that achieved by RF-sem (AUC = 0.78; p < 0.0001). On test data, however, RM-sem model (AUC = 0.82) out-performed RM-rad and RM-all (AUC = 0.5 and AUC = 0.56; p < 0.0001), neither of which was significantly different from random guess ( p = 0.9 and 0.6 respectively). Conclusion: Non-invasive classification of NSCLC can be done accurately using random forest classification models based on well-known CT-derived descriptive features. However, radiomics-based classification models performed poorly in this scenario when tested on independent data and should be used with caution, due to their possible lack of generalizability to new data. Advances in knowledge: Our study describes novel CT-derived random forest models based on radiologist-interpretation of CT scans (semantic features) that can assist NSCLC classification when histopathology is equivocal or when histopathological sampling is not possible. It also shows that random forest models based on semantic features may be more useful than those built from computational radiomic features.


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