Literature DB >> 29263171

Lung nodules: size still matters.

Anna Rita Larici1, Alessandra Farchione2, Paola Franchi2, Mario Ciliberto2, Giuseppe Cicchetti2, Lucio Calandriello2, Annemilia Del Ciello2, Lorenzo Bonomo2.   

Abstract

The incidence of indeterminate pulmonary nodules has risen constantly over the past few years. Determination of lung nodule malignancy is pivotal, because the early diagnosis of lung cancer could lead to a definitive intervention. According to the current international guidelines, size and growth rate represent the main indicators to determine the nature of a pulmonary nodule. However, there are some limitations in evaluating and characterising nodules when only their dimensions are taken into account. There is no single method for measuring nodules, and intrinsic errors, which can determine variations in nodule measurement and in growth assessment, do exist when performing measurements either manually or with automated or semi-automated methods. When considering subsolid nodules the presence and size of a solid component is the major determinant of malignancy and nodule management, as reported in the latest guidelines. Nevertheless, other nodule morphological characteristics have been associated with an increased risk of malignancy. In addition, the clinical context should not be overlooked in determining the probability of malignancy. Predictive models have been proposed as a potential means to overcome the limitations of a sized-based assessment of the malignancy risk for indeterminate pulmonary nodules.
Copyright ©ERS 2017.

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Year:  2017        PMID: 29263171     DOI: 10.1183/16000617.0025-2017

Source DB:  PubMed          Journal:  Eur Respir Rev        ISSN: 0905-9180


  21 in total

1.  Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans.

Authors:  Francesco Bianconi; Isabella Palumbo; Mario Luca Fravolini; Maria Rondini; Matteo Minestrini; Giulia Pascoletti; Susanna Nuvoli; Angela Spanu; Michele Scialpi; Cynthia Aristei; Barbara Palumbo
Journal:  Sensors (Basel)       Date:  2022-07-04       Impact factor: 3.847

2.  Hybrid models for lung nodule malignancy prediction utilizing convolutional neural network ensembles and clinical data.

Authors:  Rahul Paul; Matthew B Schabath; Robert Gillies; Lawrence O Hall; Dmitry B Goldgof
Journal:  J Med Imaging (Bellingham)       Date:  2020-04-06

3.  Multi-Level Cross Residual Network for Lung Nodule Classification.

Authors:  Juan Lyu; Xiaojun Bi; Sai Ho Ling
Journal:  Sensors (Basel)       Date:  2020-05-16       Impact factor: 3.576

4.  Solid Indeterminate Pulmonary Nodules Less Than or Equal to 250 mm3: Application of the Updated Fleischner Society Guidelines in Clinical Practice.

Authors:  Andrea Borghesi; Silvia Michelini; Giorgio Nocivelli; Mario Silva; Alessandra Scrimieri; Stefania Pezzotti; Roberto Maroldi; Davide Farina
Journal:  Radiol Res Pract       Date:  2019-01-03

5.  Clinical and Radiologic Features Together Better Predict Lung Nodule Malignancy in Patients with Soft-Tissue Sarcoma.

Authors:  Cecilia Tetta; Antonio Giugliano; Laura Tonetti; Michele Rocca; Alessandra Longhi; Francesco Londero; Gianmarco Parise; Orlando Parise; Linda Renata Micali; Mark La Meir; Jos G Maessen; Sandro Gelsomino
Journal:  J Clin Med       Date:  2020-04-23       Impact factor: 4.241

6.  Value of Shape and Texture Features from 18F-FDG PET/CT to Discriminate between Benign and Malignant Solitary Pulmonary Nodules: An Experimental Evaluation.

Authors:  Barbara Palumbo; Francesco Bianconi; Isabella Palumbo; Mario Luca Fravolini; Matteo Minestrini; Susanna Nuvoli; Maria Lina Stazza; Maria Rondini; Angela Spanu
Journal:  Diagnostics (Basel)       Date:  2020-09-15

7.  Early detection of lung cancer in Czech high-risk asymptomatic individuals (ELEGANCE): A study protocol.

Authors:  Lukas Lambert; Lenka Janouskova; Matej Novak; Bianka Bircakova; Zuzana Meckova; Jiri Votruba; Pavel Michalek; Andrea Burgetova
Journal:  Medicine (Baltimore)       Date:  2021-02-05       Impact factor: 1.817

8.  Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge.

Authors:  Yoganand Balagurunathan; Andrew Beers; Michael Mcnitt-Gray; Lubomir Hadjiiski; Sandy Napel; Dmitry Goldgof; Gustavo Perez; Pablo Arbelaez; Alireza Mehrtash; Tina Kapur; Ehwa Yang; Jung Won Moon; Gabriel Bernardino Perez; Ricard Delgado-Gonzalo; M Mehdi Farhangi; Amir A Amini; Renkun Ni; Xue Feng; Aditya Bagari; Kiran Vaidhya; Benjamin Veasey; Wiem Safta; Hichem Frigui; Joseph Enguehard; Ali Gholipour; Laura Silvana Castillo; Laura Alexandra Daza; Paul Pinsky; Jayashree Kalpathy-Cramer; Keyvan Farahani
Journal:  IEEE Trans Med Imaging       Date:  2021-11-30       Impact factor: 11.037

Review 9.  What's New on Quantitative CT Analysis as a Tool to Predict Growth in Persistent Pulmonary Subsolid Nodules? A Literature Review.

Authors:  Andrea Borghesi; Silvia Michelini; Salvatore Golemi; Alessandra Scrimieri; Roberto Maroldi
Journal:  Diagnostics (Basel)       Date:  2020-01-21

10.  Predictive model for the diagnosis of benign/malignant small pulmonary nodules.

Authors:  Weisong Chen; Dan Zhu; Hui Chen; Jianfeng Luo; Haiwei Fu
Journal:  Medicine (Baltimore)       Date:  2020-04       Impact factor: 1.817

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