scholarly journals RADIOLOGICAL AND PATHOLOGICAL CORRELATION OF LUNG NODULES IN A BACKGROUND OF METASTATIC DISEASE

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Azra Akhtar ◽  
Noreen Akhtar ◽  
Sajid Mushtaq ◽  
Usman Hassan ◽  
Ali Raza Khan
Keyword(s):  
Computed Tomography ◽  
Ct Scan ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Lung Nodule ◽  
Age Groups ◽  
Lung Nodules ◽  
Diagnostic Challenge ◽  
Radiographic Appearance ◽  
Histopathological Findings

Background: Computed tomography (CT) imaging has improved the chances of detecting small indeterminate (<1 cm) lung nodules. The determination of the underlying malignant or benign nature of a lung nodule poses a great diagnostic challenge and depends on a number of factors, including the radiographic appearance of nodule, the presence of non-pulmonary metastases, characteristics of growth and histological criteria. Methods: The medical records of 89 patients admitted to our specialist cancer centre between 2008 and 2013 were reviewed. Patients of all age groups and tumour category were included in the study. Clinical data of these patients were collected and the following parameters were analysed: Radiographic diagnosis, location, size, laterality and number of nodules and histological impression. The radiological findings were then correlated with histopathological findings. The nodules were sub-classified into groups on the basis of size (A = 0–0.5 cm; B = 0.5–0.9 cm; C = 1.0–1.5 cm and D = >1.5 cm). Results: CT scan reports of 89 patients with lung nodules were reviewed. On radiology, 73/89 (82%) were reported to be malignant nodule. Histopathological review of the biopsies of these 89 nodules confirmed malignancy in 50/89 (56.2%) patients. CT scan was found to be highly sensitive (94%, 95% confidence interval [CI]: 83.43–98.68%) but with a very low specificity (33.3%, 95% CI: 19.10–50.22%). CT scan was found to have a higher negative predictive value (81.2%, 95% CI: 54.34–95.73%) and a lower positive predictive value 64.4% (95% CI: 52.31–75.25%) when correlated with histopathological findings. Pathology of these nodules included metastatic sarcoma (27/89; 30.3%) and carcinoma (18/89; 20.2%). The frequency of the biopsy-proven malignant nodules on the right side was 26/45 (57.8%) and on the left side was 24/44 (54.5%) (P = 0.832). Malignant nodules were more frequent in lower lobes (28/43, 65.1%) than in upper lobes (14/32, 43.8%). These two sites combined accounted for 84% of all malignant nodules. There was a significant correlation between nodule size and likelihood of underlying malignancy. The overall prevalence of malignancy in the larger nodules (C and D) was much higher (23/30 and 76.7%) compared to the smaller sized (A and B) nodules (27/58 and 46.8%), P < 0.05.Conclusion: CT scan is a useful tool in the initial clinical assessment of indeterminate lung nodules with high sensitivity (94%) and a high negative predictive value (81.2%).Key words: Computed tomography, fibrosis, indeterminate lung nodule, infection, lung nodule, malignancy, metastases

scholarly journals An Efficient DA-Net Architecture for Lung Nodule Segmentation

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1457
Author(s):  
Muazzam Maqsood ◽  
Sadaf Yasmin ◽  
Irfan Mehmood ◽  
Maryam Bukhari ◽  
Mucheol Kim
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Ct Scan ◽  
Cancer Diagnosis ◽  
Lung Nodule ◽  
Search Space ◽  
Lung Nodules ◽  
Suggested Model ◽  
Lung Cancer Diagnosis ◽  
Segmentation Framework

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

scholarly journals Sensitivity of Computer-Aided Detection (CAD) Device for Lung Nodule Detection on Chest Radiography: A Real-Life Application

2020 ◽  
pp. 1-5
Author(s):  
Ammar Chaudhry ◽  
Ammar Chaudhry ◽  
William H. Moore
Keyword(s):  
Sensitivity And Specificity ◽  
Chest Radiography ◽  
Predictive Value ◽  
Lung Nodule ◽  
Chest Radiographs ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
Cad System ◽  
Computer Aided ◽  
The Mean

Purpose: The radiographic diagnosis of lung nodules is associated with low sensitivity and specificity. Computer-aided detection (CAD) system has been shown to have higher accuracy in the detection of lung nodules. The purpose of this study is to assess the effect on sensitivity and specificity when a CAD system is used to review chest radiographs in real-time setting. Methods: Sixty-three patients, including 24 controls, who had chest radiographs and CT within three months were included in this study. Three radiologists were presented chest radiographs without CAD and were asked to mark all lung nodules. Then the radiologists were allowed to see the CAD region-of-interest (ROI) marks and were asked to agree or disagree with the marks. All marks were correlated with CT studies. Results: The mean sensitivity of the three radiologists without CAD was 16.1%, which showed a statistically significant improvement to 22.5% with CAD. The mean specificity of the three radiologists was 52.5% without CAD and decreased to 48.1% with CAD. There was no significant change in the positive predictive value or negative predictive value. Conclusion: The addition of a CAD system to chest radiography interpretation statistically improves the detection of lung nodules without affecting its specificity. Thus suggesting CAD would improve overall detection of lung nodules.

Ultrasound Detection of Pneumothorax Compared with Chest X-Ray and Computed Tomography Scan

2011 ◽  
Vol 77 (4) ◽  
pp. 480-483 ◽  
Author(s):  
Khanjan Nagarsheth ◽  
Stanley Kurek
Keyword(s):  
Ct Scan ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Chest Ct ◽  
Trauma Patients ◽  
X Rays ◽  
Thoracic Ultrasound ◽  
X Ray ◽  
Chest X Ray ◽  
Chest Ct Scan

Pneumothorax after trauma can be a life threatening injury and its care requires expeditious and accurate diagnosis and possible intervention. We performed a prospective, single blinded study with convenience sampling at a Level I trauma center comparing thoracic ultrasound with chest X-ray and CT scan in the detection of traumatic pneumothorax. Trauma patients that received a thoracic ultrasound, chest X-ray, and chest CT scan were included in the study. The chest X-rays were read by a radiologist who was blinded to the thoracic ultrasound results. Then both were compared with CT scan results. One hundred and twenty-five patients had a thoracic ultrasound performed in the 24-month period. Forty-six patients were excluded from the study due to lack of either a chest X-ray or chest CT scan. Of the remaining 79 patients there were 22 positive pneumothorax found by CT and of those 18 (82%) were found on ultrasound and 7 (32%) were found on chest X-ray. The sensitivity of thoracic ultrasound was found to be 81.8 per cent and the specificity was found to be 100 per cent. The sensitivity of chest X-ray was found to be 31.8 per cent and again the specificity was found to be 100 per cent. The negative predictive value of thoracic ultrasound for pneumothorax was 0.934 and the negative predictive value for chest X-ray for pneumothorax was found to be 0.792. We advocate the use of chest ultrasound for detection of pneumothorax in trauma patients.

scholarly journals Magnetic Resonance Imaging Evaluation of Spinal Tumors and Its Comparison with Histopathological Findings

2018 ◽  
Vol 26 (2) ◽  
pp. 162-166
Author(s):  
Gopal Chandra Saha ◽  
Prodip Kumar Biswas ◽  
Md Motlabur Rahman ◽  
Mohammed Shahadat Hossain ◽  
Mohammad Zaid Hossain ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Spinal Tumor ◽  
Spinal Tumour ◽  
Spinal Tumors ◽  
Histopathological Findings ◽  
Medical College ◽  
Diagnostic Usefulness ◽  
Sensitivity Specificity

Objective: The objective of the study was to assess the diagnostic usefulness of MRI in evaluation of spinal tumors.Methodology: This cross-sectional study was carried out in Dhaka Medical College Hospital, Dhaka. The data was collected from July 2011 to June 2013 and total 51 patients were included in the study. Data was collected from MRI diagnosed spinal tumors who attended at Radiology and Imaging department of DMCH from OPD and indoor patients. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of MRI for evaluation of spinal tumors were detected.Results: Out of 51 patients 26 (51%) was intradural extramedullary, 13 (25.5%) was extradural and 12 (23.5%) was intramedullary. Distribution of patients according to MR diagnosis. Among the 51 patients 40 were diagnosed spinal tumor and 11 were not spinal tumor by MRI. Among the 40 spinal tumuor diagnosed 12 (23.5%) were schwannoma, 02 (3.9%) were neuro fibroma, 11(21.6%) were meningioma, 07(13.7%) were ependymoma, 05(9.8%) were astrocytoma, 02(3.9%) were metastasis and 1 (2.0%) was osteoblastoma. Out of all cases 40 were diagnosed as spinal tumour by MRI and among them 39 were confirmed by histopathological evaluation. They were true positive. One case was diagnosed as having spinal tumour by MRI but not confirmed by histopathological findings. That was false positive. Out of 11 cases of non tumour which were confirmed by MRI, 3 were confirmed as spinal tumour and 8 were non-tumour by histopathological findings. They were false negative and true negative respectively. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the MRI in the diagnosis of spinal tumour were 92.86%, 88.89%, 97.50%, 72.73% and 92.15% respectively.Conclusion: The present study conducted to assess the diagnostic usefulness of MRI in evaluation of spinal tumors among the Bangladeshi population. Study revealed high sensitivity, specificity and accuracy of the MRI in the diagnosis of spinal tumour. MRI should be the initial procedure in the evaluation of suspected tumors of the spine.J Dhaka Medical College, Vol. 26, No.2, October, 2017, Page 162-166

Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography

2020 ◽  
Vol 16 (1) ◽  
pp. 16-26 ◽  
Author(s):  
Shabana Rasheed Ziyad ◽  
Venkatachalam Radha ◽  
Thavavel Vayyapuri
Keyword(s):  
Computed Tomography ◽  
Review Paper ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. Objectives: The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. Methods: This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. Results: A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. Conclusion: The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

scholarly journals Evaluation of the risk of malignancy index in preoperative diagnosis of ovarian masses

2018 ◽  
Vol 7 (10) ◽  
pp. 4202
Author(s):  
Royyuru Suchitra ◽  
Kaustubh Burde ◽  
Nilima G. ◽  
P. L. S. Sahithi
Keyword(s):  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Low Cost ◽  
Age Groups ◽  
Screening Tests ◽  
Anatomical Location ◽  
Risk Of Malignancy ◽  
Risk Of Malignancy Index ◽  
Grass Root Level

Background: Ovarian cancer possesses a challenge to screening tests due to its anatomical location, poor natural history, lack of specific lesion, symptoms and signs and low prevalence. Authors shall be considering RMI 2 and RMI 4 (forms of RMI) and comparing them with histopathology report to derive the sensitivity, specificity and other parameters of these tests.Methods: A prospective   study was conducted from September 2016- September 2017 at Mazumdar Shaw Hospital, Narayana Hrudayalaya, Bangalore.73 patients met the inclusion criteria. RMI 2   and RMI4 were calculated for all the patients and these scores were compared to the final histopathology reports.Results: In present study of 73 patients RMI2 showed a sensitivity of 86.6%, specificity of 86.5 %, Positive predictive value of 81.25% and negative predictive value of 90.24 %. Whereas RMI4 showed a sensitivity of 86.6%, specificity of 86.5 %, Positive predictive value of 83.87 and negative predictive value of 90.48 %. These results are comparable to other studies conducted.  The risk of malignancy index 2 and 4 are also almost comparable with each other and so either can be used for determining the risk of malignancy in patients with adnexal masses. These results were derived in an Indian population across all age groups showing that authors can apply this low-cost method even in resource limited settings.Conclusions: Authors found that Risk of malignancy index is a simple and affordable method to determine the likelihood of a patient having adnexal mass to be malignant. This can thus help save the resources and make the services available at grass root level.

scholarly journals Negative predictive value of preoperative computed tomography in determining pathologic local invasion, nodal disease, and abdominal metastases in gastric cancer

2016 ◽  
Vol 23 (4) ◽  
pp. 273 ◽  
Author(s):  
D.J. Kagedan ◽  
F. Frankul ◽  
A. El-Sedfy ◽  
C. McGregor ◽  
M. Elmi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Population Level ◽  
Distant Metastases ◽  
Ct Imaging ◽  
Abdominal Ct ◽  
Curative Intent ◽  
Local Invasion ◽  
Pathology Reports

BackgroundBefore undergoing curative-intent resection of gastric adenocarcinoma (ga), most patients undergo abdominal computed tomography (ct) imaging to determine contraindications to resection (local invasion, distant metastases). However, the ability to detect contraindications is variable, and the literature is limited to single-institution studies. We sought to assess, on a population level, the clinical relevance of preoperative ct in evaluating the resectability of ga tumours in patients undergoing surgery.Methods In a provincial cancer registry, 2414 patients with ga diagnosed during 2005–2008 at 116 institutions were identified, and a primary chart review of radiology, operative, and pathology reports was performed for all patients. Preoperative abdominal ct reports were compared with intraoperative findings and final pathology reports (reference standard) to determine the negative predictive value (npv) of ct in assessing local invasion, nodal involvement, and intra-abdominal metastases.Results Among patients undergoing gastrectomy, the npv of ct imaging in detecting local invasion was 86.9% (n = 536). For nodal metastasis, the npv of ct was 43.3% (n = 450). Among patients undergoing surgical exploration, the npv of ct for intra-abdominal metastases was 52.3% (n = 407).Conclusions Preoperative abdominal ct imaging reported as negative is most accurate in determining local invasion and least accurate in nodal assessment. The poor npv of ct should be taken into account when selecting patients for staging laparoscopy.

Educational Review of Predictive Value and Findings of Computed Tomography Scan in Diagnosing Bowel and Mesenteric Injuries after Blunt Trauma: Correlation with Trauma Surgery Findings in 163 Patients

2017 ◽  
Vol 68 (3) ◽  
pp. 276-285 ◽  
Author(s):  
Francesco Cinquantini ◽  
Gregorio Tugnoli ◽  
Alice Piccinini ◽  
Carlo Coniglio ◽  
Sergio Mannone ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Ct Scan ◽  
Abdominal Trauma ◽  
Blunt Abdominal Trauma ◽  
Predictive Value ◽  
Gold Standard ◽  
Clinical Management ◽  
Trauma Surgery ◽  
Trauma Patients ◽  
Increased Risk

Background and Aims Laparotomy can detect bowel and mesenteric injuries in 1.2%–5% of patients following blunt abdominal trauma. Delayed diagnosis in such cases is strongly related to increased risk of ongoing sepsis, with subsequent higher morbidity and mortality. Computed tomography (CT) scanning is the gold standard in the evaluation of blunt abdominal trauma, being accurate in the diagnosis of bowel and mesenteric injuries in case of hemodynamically stable trauma patients. Aims of the present study are to 1) review the correlation between CT signs and intraoperative findings in case of bowel and mesenteric injuries following blunt abdominal trauma, analysing the correlation between radiological features and intraoperative findings from our experience on 25 trauma patients with small bowel and mesenteric injuries (SBMI); 2) identify the diagnostic specificity of those signs found at CT with practical considerations on the following clinical management; and 3) distinguish the bowel and mesenteric injuries requiring immediate surgical intervention from those amenable to initial nonoperative management. Materials and Methods Between January 1, 2008, and May 31, 2010, 163 patients required laparotomy following blunt abdominal trauma. Among them, 25 patients presented bowel or mesenteric injuries. Data were analysed retrospectively, correlating operative surgical reports with the preoperative CT findings. Results We are presenting a pictorial review of significant and frequent findings of bowel and mesenteric lesions at CT scan, confirmed intraoperatively at laparotomy. Moreover, the predictive value of CT scan for SBMI is assessed. Conclusions Multidetector CT scan is the gold standard in the assessment of intra-abdominal blunt abdominal trauma for not only parenchymal organs injuries but also detecting SBMI; in the presence of specific signs it provides an accurate assessment of hollow viscus injuries, helping the trauma surgeons to choose the correct initial clinical management.

scholarly journals Lung Nodule Detection using Segmentation Approach for Computed Tomography Scan Images

2021 ◽  
Vol 9 (9) ◽  
pp. 1778-1790
Author(s):  
Henil Satra
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Ct Scan ◽  
Lung Nodule ◽  
Morphological Operations ◽  
Lung Nodule Detection ◽  
Contour Segmentation ◽  
Nodule Detection ◽  
Segmentation Approach ◽  
Computed Tomography Image

Abstract: Lung disorders have become really common in today’s world due to growing amount of air pollution, our increased exposure to harmful radiations and our unhealthy lifestyles. Hence, the diagnosis of lung disorders has become of paramount importance. The commonly used Thresholding approaches and morphological operations often fail to detect the peripheral pathology bearing areas. Hence, we present the segmentation approach of the lung tissue for computer aided diagnosis system. We use a novel technique for segmentation of lungs from CT scan (Computed Tomography) of the chest or upper torso. The accuracy of analysis and its implication majorly depends on the kind of segmentation technique used. Hence, it is important that the method used is highly reliable and is successful in nodule detection and classification. We use MATLAB and OpenCV libraries to apply segmentation on CT scan images to get the desired output. We have also created a working proprietary user interface called “PULMONIS” for the ease of doctors and patients to upload the CT scan images and get the output after the image processing is done in the backend. Keywords: Lung nodule detection, Image Processing, Computed Tomography, Image Segmentation, Lung Cancer, Contour Segmentation, MATLAB, OpenCV, Computer Vision.

scholarly journals Evaluation of Chest CT Scan as a Screening and Diagnostic Tool in Trauma Patients with Coronavirus Disease 2019 (COVID-19): A Cross-Sectional Study

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hossein Abdolrahimzadeh Fard ◽  
Salahaddin Mahmudi-Azer ◽  
Sepideh Sefidbakht ◽  
Pooya Iranpour ◽  
Shahram Bolandparvaz ◽  
...  
Keyword(s):  
Ct Scan ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Cross Sectional Study ◽  
Chest Ct ◽  
Trauma Patients ◽  
Ct Scanner ◽  
Sectional Study ◽  
Cross Sectional ◽  
Chest Ct Scan

Background. The lack of enough medical evidence about COVID-19 regarding optimal prevention, diagnosis, and treatment contributes negatively to the rapid increase in the number of cases globally. A chest computerized tomography (CT) scan has been introduced as the most sensitive diagnostic method. Therefore, this research aimed to examine and evaluate the chest CT  scan as a screening measure of COVID-19 in trauma patients. Methods. This cross-sectional study was conducted in Rajaee Hospital in Shiraz from February to May 2020. All patients underwent unenhanced CT with a 16-slice CT scanner. The CT scans were evaluated in a blinded manner, and the main CT scan features were described and classified into four groups according to RSNA recommendation. Subsequently, the first two Radiological Society of North America (RSNA) categories with the highest probability of COVID-19 pneumonia (i.e., typical and indeterminate) were merged into the “positive CT scan group” and those with radiologic features with the least probability of COVID-19 pneumonia into “negative CT scan group.” Results. Chest CT scan had a sensitivity of 68%, specificity of 56%, positive predictive value of 34.8%, negative predictive value of 83.7%, and accuracy of 59.3% in detecting COVID-19 among trauma patients. Moreover, for the diagnosis of COVID-19 by CT scan in asymptomatic individuals, a sensitivity of 100%, specificity of 66.7%, and negative predictive value of 100% were obtained ( p value: 0.05). Conclusion. Findings of the study indicated that the CT scan’s sensitivity and specificity is less effective in diagnosing trauma patients with COVID-19 compared with nontraumatic people.

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