Cargando…

The Diagnostic Accuracy of a Novel Scoring System Using Multi-Detector Computed Tomography to Diagnose Lung Cancer

Introduction: Lung cancer is the leading cause amongst the cancer deaths in the world. Detection of malignancy at an early stage and with precision is the utmost objective of radiological evaluation. The final diagnosis of lung cancer is histopathological evaluation of the mass. The authors hereby h...

Descripción completa

Detalles Bibliográficos
Autores principales: Bhagat, Saurav, Gupta, Vishal, Jain, Sujeet Kumar, Aaggarwal, Sakshi, Khanduri, Sachin, Batra, Saumay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cureus 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10076812/
https://www.ncbi.nlm.nih.gov/pubmed/37033527
http://dx.doi.org/10.7759/cureus.35848
_version_ 1785020217258147840
author Bhagat, Saurav
Gupta, Vishal
Jain, Sujeet Kumar
Aaggarwal, Sakshi
Khanduri, Sachin
Batra, Saumay
author_facet Bhagat, Saurav
Gupta, Vishal
Jain, Sujeet Kumar
Aaggarwal, Sakshi
Khanduri, Sachin
Batra, Saumay
author_sort Bhagat, Saurav
collection PubMed
description Introduction: Lung cancer is the leading cause amongst the cancer deaths in the world. Detection of malignancy at an early stage and with precision is the utmost objective of radiological evaluation. The final diagnosis of lung cancer is histopathological evaluation of the mass. The authors hereby have tried to convert the multi-detector CT (MDCT) characteristics and patient demographics into quantitative data to formulate a scoring system that can predict lung malignancy as close to histopathology as possible. Materials and methods: After obtaining ethical clearance, 104 cases of suspected lung cancer by history, clinical and radiographic evaluation were enrolled in the study. These patients were undergoing CT thorax (contrast) on 384 slice siemens somatom force. After undergoing the radiological evaluation biopsy of the mass was done either by CT guided or bronchoscopy guided. Radiological and histopathological findings were correlated. Patients aged >50, lymphadenopathy, tumor volume >50 cc, enhancement >15 HU (Hounsfield unit) after contrast injection were given a score of 15 each. History of smoking, bronchus cut off, spiculated/lobulated margins, mediastinal/pleural involvement, and angiogram sign positive were given a score of 20 each. So, a maximum score of 160 can be achieved by history and MDCT evaluation. Results: Sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and diagnostic accuracy of MDCT by using conventional parameters against histopathology was 97.5%, 85%, 96.29%, 89.47%, and 95.0%. The sensitivity and specificity calculated through Receiver-Operating-Characteristic (ROC) for predicting malignancy were found to be 98.8% and 90.0% for a cut-off score of >97.5 out of maximum of 160.  Conclusion: MDCT serves as a tool for early diagnosis of lung cancer, and it is the utmost important tool for cases where biopsy or fine needle aspiration cytology (FNAC) is not possible. By creating a quantitative criterion to diagnose lung malignancy, the subjective nature of MDCT diagnosis can be converted into an objective based evaluation.
format Online
Article
Text
id pubmed-10076812
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Cureus
record_format MEDLINE/PubMed
spelling pubmed-100768122023-04-07 The Diagnostic Accuracy of a Novel Scoring System Using Multi-Detector Computed Tomography to Diagnose Lung Cancer Bhagat, Saurav Gupta, Vishal Jain, Sujeet Kumar Aaggarwal, Sakshi Khanduri, Sachin Batra, Saumay Cureus Radiology Introduction: Lung cancer is the leading cause amongst the cancer deaths in the world. Detection of malignancy at an early stage and with precision is the utmost objective of radiological evaluation. The final diagnosis of lung cancer is histopathological evaluation of the mass. The authors hereby have tried to convert the multi-detector CT (MDCT) characteristics and patient demographics into quantitative data to formulate a scoring system that can predict lung malignancy as close to histopathology as possible. Materials and methods: After obtaining ethical clearance, 104 cases of suspected lung cancer by history, clinical and radiographic evaluation were enrolled in the study. These patients were undergoing CT thorax (contrast) on 384 slice siemens somatom force. After undergoing the radiological evaluation biopsy of the mass was done either by CT guided or bronchoscopy guided. Radiological and histopathological findings were correlated. Patients aged >50, lymphadenopathy, tumor volume >50 cc, enhancement >15 HU (Hounsfield unit) after contrast injection were given a score of 15 each. History of smoking, bronchus cut off, spiculated/lobulated margins, mediastinal/pleural involvement, and angiogram sign positive were given a score of 20 each. So, a maximum score of 160 can be achieved by history and MDCT evaluation. Results: Sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and diagnostic accuracy of MDCT by using conventional parameters against histopathology was 97.5%, 85%, 96.29%, 89.47%, and 95.0%. The sensitivity and specificity calculated through Receiver-Operating-Characteristic (ROC) for predicting malignancy were found to be 98.8% and 90.0% for a cut-off score of >97.5 out of maximum of 160.  Conclusion: MDCT serves as a tool for early diagnosis of lung cancer, and it is the utmost important tool for cases where biopsy or fine needle aspiration cytology (FNAC) is not possible. By creating a quantitative criterion to diagnose lung malignancy, the subjective nature of MDCT diagnosis can be converted into an objective based evaluation. Cureus 2023-03-06 /pmc/articles/PMC10076812/ /pubmed/37033527 http://dx.doi.org/10.7759/cureus.35848 Text en Copyright © 2023, Bhagat et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Radiology
Bhagat, Saurav
Gupta, Vishal
Jain, Sujeet Kumar
Aaggarwal, Sakshi
Khanduri, Sachin
Batra, Saumay
The Diagnostic Accuracy of a Novel Scoring System Using Multi-Detector Computed Tomography to Diagnose Lung Cancer
title The Diagnostic Accuracy of a Novel Scoring System Using Multi-Detector Computed Tomography to Diagnose Lung Cancer
title_full The Diagnostic Accuracy of a Novel Scoring System Using Multi-Detector Computed Tomography to Diagnose Lung Cancer
title_fullStr The Diagnostic Accuracy of a Novel Scoring System Using Multi-Detector Computed Tomography to Diagnose Lung Cancer
title_full_unstemmed The Diagnostic Accuracy of a Novel Scoring System Using Multi-Detector Computed Tomography to Diagnose Lung Cancer
title_short The Diagnostic Accuracy of a Novel Scoring System Using Multi-Detector Computed Tomography to Diagnose Lung Cancer
title_sort diagnostic accuracy of a novel scoring system using multi-detector computed tomography to diagnose lung cancer
topic Radiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10076812/
https://www.ncbi.nlm.nih.gov/pubmed/37033527
http://dx.doi.org/10.7759/cureus.35848
work_keys_str_mv AT bhagatsaurav thediagnosticaccuracyofanovelscoringsystemusingmultidetectorcomputedtomographytodiagnoselungcancer
AT guptavishal thediagnosticaccuracyofanovelscoringsystemusingmultidetectorcomputedtomographytodiagnoselungcancer
AT jainsujeetkumar thediagnosticaccuracyofanovelscoringsystemusingmultidetectorcomputedtomographytodiagnoselungcancer
AT aaggarwalsakshi thediagnosticaccuracyofanovelscoringsystemusingmultidetectorcomputedtomographytodiagnoselungcancer
AT khandurisachin thediagnosticaccuracyofanovelscoringsystemusingmultidetectorcomputedtomographytodiagnoselungcancer
AT batrasaumay thediagnosticaccuracyofanovelscoringsystemusingmultidetectorcomputedtomographytodiagnoselungcancer
AT bhagatsaurav diagnosticaccuracyofanovelscoringsystemusingmultidetectorcomputedtomographytodiagnoselungcancer
AT guptavishal diagnosticaccuracyofanovelscoringsystemusingmultidetectorcomputedtomographytodiagnoselungcancer
AT jainsujeetkumar diagnosticaccuracyofanovelscoringsystemusingmultidetectorcomputedtomographytodiagnoselungcancer
AT aaggarwalsakshi diagnosticaccuracyofanovelscoringsystemusingmultidetectorcomputedtomographytodiagnoselungcancer
AT khandurisachin diagnosticaccuracyofanovelscoringsystemusingmultidetectorcomputedtomographytodiagnoselungcancer
AT batrasaumay diagnosticaccuracyofanovelscoringsystemusingmultidetectorcomputedtomographytodiagnoselungcancer