Objective: This study aimed to develop and validate predictive models using clinical
parameters, radiomic features, and a combination of both for invasive mucinous …

Objective: To develop and validate predictive models based on clinical parameters, and
radiomic features to distinguish pulmonary pure invasive mucinous adenocarcinoma (pIMA) …

Objective To investigate computed tomography (CT) features of stage IA invasive mucinous
adenocarcinoma (IMA) of the lung and establish a predictive model. Methods Fifty-three …

Objective In this study, we aimed to utilize computed tomography (CT)-derived radiomics
and various machine learning approaches to differentiate between invasive mucinous …

Purpose The purpose of this study was to distinguish pneumonic-type mucinous
adenocarcinoma (PTMA) from lobar pneumonia (LP) by pre-treatment CT radiological and …

Background As an emerging technology, radiomics is being widely used in the diagnosis of
early lung cancer due to its excellent diagnostic performance. However, there is a lack of …

Please see the Editorial Comment by Lydia Chelala discussing this article. To listen to the
podcast associated with this article, please select one of the following: iTunes, Google Play …

Background To build prediction models with radiomics features, clinical/conventional
radiographic signs and combined scores for the discrimination of micropapillary or solid …

Objective To develop and validate predictive models using clinical parameters, radiomic
features and a combination of both for preoperative differentiation of pulmonary nodular …

Background At present, there is a lack of studies in invasive mucinous adenocarcinoma
(IMA) that combine clinicopathological and imaging features to stratify risk and select optimal …