A Subsolid Nodules Imaging Reporting System (SSN-IRS) for Classifying 3 Subtypes of Pulmonary Adenocarcinoma

Xiaonan Cui; Marjolein A Heuvelmans; Shuxuan Fan; Daiwei Han; Sunyi Zheng; Yihui Du; Yingru Zhao; Grigory Sidorenkov; Harry J M Groen; Monique D Dorrius; Matthijs Oudkerk; Geertruida H de Bock; Rozemarijn Vliegenthart; Zhaoxiang Ye

doi:10.1016/j.cllc.2020.01.014

A Subsolid Nodules Imaging Reporting System (SSN-IRS) for Classifying 3 Subtypes of Pulmonary Adenocarcinoma

Xiaonan Cui, Marjolein A Heuvelmans, Shuxuan Fan, Daiwei Han, Sunyi Zheng, Yihui Du, Yingru Zhao, Grigory Sidorenkov, Harry J M Groen, Monique D Dorrius, Matthijs Oudkerk, Geertruida H de Bock, Rozemarijn Vliegenthart, Zhaoxiang Ye^*

^*Bijbehorende auteur voor dit werk

Onderzoeksoutput › Academic › peer review

3 Citaten (Scopus)

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Samenvatting

It is essential to identify the subsolid nodules subtype preoperatively to select the optimal treatment algorithm. We developed and validated an imaging reporting system using a classification and regression tree model that based on computed tomography imaging characteristics (291 cases in training group, 146 cases in testing group). The model showed high sensitivity and accuracy of classification. Our model can help clinicians to make follow-up recommendations or decisions for surgery for clinical patients with a subsolid nodule.

Originele taal-2	English
Pagina's (van-tot)	314-+
Aantal pagina's	16
Tijdschrift	Clinical lung cancer
Volume	21
Nummer van het tijdschrift	4
Vroegere onlinedatum	6-feb.-2020
DOI's	https://doi.org/10.1016/j.cllc.2020.01.014
Status	Published - jul.-2020

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10.1016/j.cllc.2020.01.014Licentie: CC BY-NC-ND

A Subsolid Nodules Imaging Reporting System (SSN-IRS) for Classifying 3 Subtypes of Pulmonary AdenocarcinomaFinal publisher's version, 5,9 MBLicentie: CC BY-NC-ND

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Cui, X., Heuvelmans, M. A., Fan, S., Han, D., Zheng, S., Du, Y., Zhao, Y., Sidorenkov, G., Groen, H. J. M., Dorrius, M. D., Oudkerk, M., de Bock, G. H., Vliegenthart, R., & Ye, Z. (2020). A Subsolid Nodules Imaging Reporting System (SSN-IRS) for Classifying 3 Subtypes of Pulmonary Adenocarcinoma. Clinical lung cancer, 21(4), 314-+. https://doi.org/10.1016/j.cllc.2020.01.014

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title = "A Subsolid Nodules Imaging Reporting System (SSN-IRS) for Classifying 3 Subtypes of Pulmonary Adenocarcinoma",

abstract = "It is essential to identify the subsolid nodules subtype preoperatively to select the optimal treatment algorithm. We developed and validated an imaging reporting system using a classification and regression tree model that based on computed tomography imaging characteristics (291 cases in training group, 146 cases in testing group). The model showed high sensitivity and accuracy of classification. Our model can help clinicians to make follow-up recommendations or decisions for surgery for clinical patients with a subsolid nodule.",

keywords = "Decision trees, Diagnosis, Lung, Solitary pulmonary nodule, X-ray computed tomography, GROUND-GLASS OPACITY, MINIMALLY INVASIVE ADENOCARCINOMA, DECISION TREE, LUNG, CT, CLASSIFICATION, DIAGNOSIS, RESECTION, SECTION, CANCER",

author = "Xiaonan Cui and Heuvelmans, {Marjolein A} and Shuxuan Fan and Daiwei Han and Sunyi Zheng and Yihui Du and Yingru Zhao and Grigory Sidorenkov and Groen, {Harry J M} and Dorrius, {Monique D} and Matthijs Oudkerk and {de Bock}, {Geertruida H} and Rozemarijn Vliegenthart and Zhaoxiang Ye",

year = "2020",

month = jul,

doi = "10.1016/j.cllc.2020.01.014",

language = "English",

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T1 - A Subsolid Nodules Imaging Reporting System (SSN-IRS) for Classifying 3 Subtypes of Pulmonary Adenocarcinoma

AU - Cui, Xiaonan

AU - Heuvelmans, Marjolein A

AU - Fan, Shuxuan

AU - Han, Daiwei

AU - Zheng, Sunyi

AU - Du, Yihui

AU - Zhao, Yingru

AU - Sidorenkov, Grigory

AU - Groen, Harry J M

AU - Dorrius, Monique D

AU - Oudkerk, Matthijs

AU - de Bock, Geertruida H

AU - Vliegenthart, Rozemarijn

AU - Ye, Zhaoxiang

PY - 2020/7

Y1 - 2020/7

N2 - It is essential to identify the subsolid nodules subtype preoperatively to select the optimal treatment algorithm. We developed and validated an imaging reporting system using a classification and regression tree model that based on computed tomography imaging characteristics (291 cases in training group, 146 cases in testing group). The model showed high sensitivity and accuracy of classification. Our model can help clinicians to make follow-up recommendations or decisions for surgery for clinical patients with a subsolid nodule.

AB - It is essential to identify the subsolid nodules subtype preoperatively to select the optimal treatment algorithm. We developed and validated an imaging reporting system using a classification and regression tree model that based on computed tomography imaging characteristics (291 cases in training group, 146 cases in testing group). The model showed high sensitivity and accuracy of classification. Our model can help clinicians to make follow-up recommendations or decisions for surgery for clinical patients with a subsolid nodule.

KW - Decision trees

KW - Diagnosis

KW - Lung

KW - Solitary pulmonary nodule

KW - X-ray computed tomography

KW - GROUND-GLASS OPACITY

KW - MINIMALLY INVASIVE ADENOCARCINOMA

KW - DECISION TREE

KW - LUNG

KW - CT

KW - CLASSIFICATION

KW - DIAGNOSIS

KW - RESECTION

KW - SECTION

KW - CANCER

U2 - 10.1016/j.cllc.2020.01.014

DO - 10.1016/j.cllc.2020.01.014

M3 - Article

C2 - 32273256

VL - 21

SP - 314-+

JO - Clinical lung cancer

JF - Clinical lung cancer

SN - 1525-7304

IS - 4

ER -