scholarly journals Of multidetector computed tomography (MDCT) in evaluation of lung nodule with histopathological correlation

2021 ◽  
Vol 52 (3) ◽  
pp. 193-198
Author(s):  
Jagmohan Gupta ◽  
Parul Gupta ◽  
Suresh Gupta ◽  
Amit Tak
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Lymph Nodes ◽  
Diagnostic Accuracy ◽  
Multidetector Ct ◽  
Histopathological Examination ◽  
Lung Nodule ◽  
Invasive Procedure ◽  
Histopathological Analysis ◽  
Ct Guided

Background: Lung cancer is the most common and lethal cancer around the world. Computed tomography (CT) is an integral imaging technique for staging the lung cancer. Aim of this study was to correlate the multidetector CT (MDCT) findings of lung nodule with histopathological examination, as well as to assess the diagnostic accuracy of MDCT in evaluation of suspected lung nodule. Methods: One hundred patients with clinical or radiological suspicion of lung nodule referred for CT scan of thorax were included in the study. Histopathological analysis was performed. The location of the lesion was analysed and nodules were classified. Fine needle aspiration cytology (FNAC) was done with spinal needle under all aseptic precautions. The results obtained by MDCT were analysed and compared with histopathological findings done by CT guided FNAC. Results: Average age of patients was 65 years, 25 % were females and 75 % were males. Among all the patients 66 % of lesions were located in right side lung and 34 % of lesions were left in location. Of all, 2 % patients had lesions less than 3 cm, 11 % patients had lesion between 3-4 cm, 19 % patients had lesion between 4-5 cm, 26 % patients had lesion between 5-7 cm and 42 % patients had lesion greater than 7 cm. Many of these patients also presented with enlarged lymph nodes, most commonly mediastinal (73 %) followed by subcarinal (51 %), hilar (44 %) and supraclavicular (4 %) lymph nodes. The most common histological findings of lung nodules analysis were adenocarcinoma (41 %). Among the 100 patients 58 % had lesions located peripherally while 42 % had central lesions. CT was a highly sensitive (95.45 %) and moderately specific (75 %) test and also had a high positive predictive value (96 %) to diagnose malignant lung nodule. Conclusion: CT guided FNAC of lung nodule is a safe, minimal invasive procedure with a high diagnostic accuracy. The use of CT-guided FNAC in hilar and mediastinal nodules can avoid unnecessary exploratory surgery for staging and also diagnosis could be made with lesser cost.

scholarly journals P07.07 Computed Tomography of Lymph Nodes to Predict Occult pN2 Disease in Non-Small-Cell Lung Cancer

2021 ◽  
Vol 16 (3) ◽  
pp. S280
Author(s):  
H. Onozawa ◽  
D. Nemoto ◽  
J. Miura ◽  
D. Eriguchi ◽  
H. Adachi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Lymph Nodes ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung

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.

Staging of primary lung cancer by computed tomography-guided percutaneous needle cytology of mediastinal lymph nodes

1996 ◽  
Vol 62 (2) ◽  
pp. 352-355 ◽  
Author(s):  
Hideki Akamatsu ◽  
Masanori Terashima ◽  
Teruaki Koike ◽  
Tsuneyo Takizawa ◽  
Yuzo Kurita
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Lymph Nodes ◽  
Primary Lung Cancer ◽  
Mediastinal Lymph Nodes

“ROLE OF FNAC IN THE DIAGNOSIS OF SALIVARY GLAND LESION”

2021 ◽  
pp. 69-71
Author(s):  
Rifat Qureishi ◽  
M.H. Usmani ◽  
U.R. Singh ◽  
P.C. Kol
Keyword(s):  
Salivary Gland ◽  
Diagnostic Accuracy ◽  
Malignant Tumors ◽  
Histopathological Examination ◽  
Standard Procedure ◽  
Neck Region ◽  
Invasive Procedure ◽  
Sensitivity Index ◽  
Cytologic Diagnosis ◽  
Wide Range

Background: Fine needle aspiration cytology (FNAC) has been used for diagnosis of salivary gland lesions for many years. Various studies in the existing literature have shown a wide range of sensitivity and diagnostic accuracy of cytologic diagnosis. FNAC is a safe, simple, cost effective, 1-4 accurate and minimal invasive procedure for the evaluation of salivary gland lesions. FNAC is not only useful in planning denitive preoperative 2,5-6 diagnosis but also can prevent unnecessary surgical intervention. Salivary gland swelling occur more commonly in 3rd decade of life with equal sex incidence. Parotid is one of the most commonly involved glands in the head & neck region swellings. FNAC appears to be highly sensitive for benign tumours and highly specic for malignant tumors and it should be the rst line of investigation in evaluating the salivary gland pathologies. 7 Early diagnosis and appropriate management carries good prognosis. Methods: Patients with suspected salivary gland enlargements, referred for FNAC, were included in this study. FNAC was performed by using the standard procedure. Cytologic diagnosis was compared with histopathologic diagnosis wherever it was available. Results: In the present study conducted in the department of pathology, Shyam Shah Medical College Rewa MP, over a period of 5 years, 152 Patients with suspected salivary gland enlargements were retrospectively appraised. The benign lesions of salivary gland were 133 (87.5%), out of which Pleomorphic adenoma was diagnosed in 75 (56.39%) cases, clinical correlation was found in 58 (77.33%) cases. 29 cases were subsequently correlated with histopathological examination 26 correctly correlated and the diagnostic accuracy of FNAC was found to be 89.65%. Inammatory lesions were proved correct in 08 out of 10 cases after histopathology. Therefore, accuracy of FNAC was 80.0%. Malignant lesions of salivary gland were found in 19 cases, out of which 15 (78.94%) cases correlated with clinical diagnosis. 13 cases were subjected to histopathology 10 correctly correlated and the diagnostic accuracy of FNAC was found to be 76.92%. Overall diagnostic accuracy of FNAC was 84.61%. Conclusion:In conclusions it can be established that FNAC is an efcient and accurate procedure with high sensitivity index, and its usefulness is enhanced due to it being a relatively easy procedure which can be carried out even on outdoor patients.

scholarly journals From Raw Pixels to Recurrence Image for Deep Learning of Benign and Malignant Mediastinal Lymph Nodes on Computed Tomography

2020 ◽  
Author(s):  
Tuan Pham
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Mediastinal Lymph Node ◽  
Survival Rates ◽  
Primary Lung Cancer ◽  
Lymph Node Status ◽  
Mediastinal Lymph Nodes

<div>Lung cancer causes the most cancer deaths worldwide and has one of the lowest five-year survival rates of all cancer types. It is reported that more than half of patients with lung cancer die within one year of being diagnosed. Because mediastinal lymph node status is the most important factor for the treatment and prognosis of lung cancer, the aim of this study is to improve the predictive value in assessing the computed tomography (CT) of mediastinal lymph-node malignancy in patients with primary lung cancer. This paper introduces a new method for creating pseudo-labeled images of CT regions of mediastinal lymph nodes by using the concept of recurrence analysis in nonlinear dynamics for the transfer learning. Pseudo-labeled images of original CT images are used as input into deep-learning models. Three popular pretrained convolutional neural networks (AlexNet, SqueezeNet, and DenseNet-201) were used for the implementation of the proposed concept for the classification of benign and malignant mediastinal lymph nodes using a public CT database. In comparison with the use of the original CT data, the results show the high performance of the transformed images for the task of classification. The proposed method has the potential for differentiating benign from malignant mediastinal lymph nodes on CT, and may provide a new way for studying lung cancer using radiology imaging. </div><div><br></div>

scholarly journals Mediastinal Staging for Lung Cancer

2014 ◽  
Vol 21 (3) ◽  
pp. 159-161 ◽  
Author(s):  
Jacob Gelberg ◽  
Sean Grondin ◽  
Alain Tremblay
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Positron Emission Tomography ◽  
Lymph Nodes ◽  
Emission Tomography ◽  
Mediastinal Staging ◽  
Positron Emission ◽  
Pet Ct ◽  
Diagnosis Of Cancer ◽  
Rule Out

Staging of the mediastinal and hilar lymph nodes plays a crucial role in identifying the best treatment option for patients with confirmed or suspected lung cancer and, in many cases, can simultaneously confirm a diagnosis of cancer. Noninvasive modalities, such as computed tomography (CT), positron emission tomography (PET) and PET-CT, are an important first step in this assessment. Ultimately, invasive staging is frequently required to confirm or rule out the presence of metastatic disease within the lymph nodes. The present focused review describes and compares noninvasive and invasive modalities for mediastinal staging in lung cancer.

Perinodular Vascularity Distinguishes Benign Intrapulmonary Lymph Nodes From Lung Cancer on Computed Tomography

2019 ◽  
Vol 34 (5) ◽  
pp. 326-328 ◽  
Author(s):  
Joseph Barnett ◽  
Ilaria Pulzato ◽  
Ryan Wilson ◽  
Simon Padley ◽  
Andrew G. Nicholson ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Lymph Nodes

Diagnostic Accuracy of Intra-abdominal Fluid Collection Characterization in the Era of Multidetector Computed Tomography

2012 ◽  
Vol 78 (2) ◽  
pp. 185-189 ◽  
Author(s):  
Brian C. Allen ◽  
Huiman Barnhart ◽  
Mustafa Bashir ◽  
Christopher Nieman ◽  
Steven Breault ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Multidetector Ct ◽  
Imaging Finding ◽  
Fluid Collection ◽  
Ct Guided ◽  
High Attenuation ◽  
Institutional Review ◽  
Imaging Characteristics ◽  
Fluid Collections ◽  
Ct Density

The purpose of this study was to test the hypothesis that with improved technology, the presence of abscess in a postoperative fluid collection may be prospectively made. This is an Institutional Review Board-approved, Health Insurance Portability and Accountability Act-compliant retrospective review of computed tomography (CT)-guided intra-abdominal fluid collection drainages. The diagnostic CT scans of 95 consecutive patients performed on 16- and 64-multidetector CT (MDCT) were reviewed by four readers with varying abdominal imaging expertise. Readers were asked to determine fluid content, to document whether infection was present, and to rate reader confidence for infection. A fifth radiologist reviewed the collections for imaging characteristics. The gold standard for presence of infection was microbiological Gram stain and culture. The logistic regression model showed that both fluid collections containing gas or high attenuation fluid (average CT density 20 or greater Hounsfield units) are significant predictors of infection ( P = 0.001). The average sensitivity over the four readers for determining presence of infection was 83.4 per cent and specificity was 39.3 per cent. Even in the era of MDCT, the ability to predict whether or not a fluid collection is infected or not, based on imaging findings alone, is limited. Presence of gas is a strong indicator of infection, but no imaging finding is characteristic of a sterile fluid collection.

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