Prediction of micropapillary and solid pattern in lung adenocarcinoma using radiomic values extracted from near-pure histopathological subtypes.
Objectives: Near-pure lung adenocarcinoma (ADC) subtypes demonstrate strong stratification of radiomic values, providing basic information for pathological subtyping. We sought to predict the presence of high-grade (micropapillary and solid) components in lung ADCs using quantitative image analysis...
| Publicado en: | European Radiology Vol. 31; no. 7; pp. 5127 - 5139 |
|---|---|
| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150974494&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150974494 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jul2021 vid: 31 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150974494 147895513 150974494 NLM33389033 10.1007/s00330-020-07570-6 NLM33389033 150974494 ppf: 5127 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Prediction of micropapillary and solid pattern in lung adenocarcinoma using radiomic values extracted from near-pure histopathological subtypes. aug: au: Chen, Li-Wei Yang, Shun-Mao Wang, Hao-Jen Chen, Yi-Chang Lin, Mong-Wei Hsieh, Min-Shu Song, Hsiang-Lin Ko, Huan-Jang Chen, Chung-Ming Chang, Yeun-Chung affil: Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., 10617, Taipei, Taiwan sug: subj: Lung Neoplasms Image Processing, Computer Assisted Retrospective Design Lung Tomography, X-Ray Computed Clinical Assessment Tools ab: Objectives: Near-pure lung adenocarcinoma (ADC) subtypes demonstrate strong stratification of radiomic values, providing basic information for pathological subtyping. We sought to predict the presence of high-grade (micropapillary and solid) components in lung ADCs using quantitative image analysis with near-pure radiomic values.Methods: Overall, 103 patients with lung ADCs of various histological subtypes were enrolled for 10-repetition, 3-fold cross-validation (cohort 1); 55 were enrolled for testing (cohort 2). Histogram and textural features on computed tomography (CT) images were assessed based on the "near-pure" pathological subtype data. Patch-wise high-grade likelihood prediction was performed for each voxel within the tumour region. The presence of high-grade components was then determined based on a volume percentage threshold of the high-grade likelihood area. To compare with quantitative approaches, consolidation/tumour (C/T) ratio was evaluated on CT images; we applied radiological invasiveness (C/T ratio > 0.5) for the prediction.Results: In cohort 1, patch-wise prediction, combined model (C/T ratio and patch-wise prediction), whole-lesion-based prediction (using only the "near-pure"-based prediction model), and radiological invasiveness achieved a sensitivity and specificity of 88.00 ± 2.33% and 75.75 ± 2.82%, 90.00 ± 0.00%, and 77.12 ± 2.67%, 66.67% and 90.41%, and 90.00% and 45.21%, respectively. The sensitivity and specificity, respectively, for cohort 2 were 100.0% and 95.35% using patch-wise prediction, 100.0% and 95.35% using combined model, 75.00% and 95.35% using whole-lesion-based prediction, and 100.0% and 69.77% using radiological invasiveness.Conclusion: Using near-pure radiomic features and patch-wise image analysis demonstrated high levels of sensitivity and moderate levels of specificity for high-grade ADC subtype-detecting.Key Points: • The radiomic values extracted from lung adenocarcinoma with "near-pure" histological subtypes provide useful information for high-grade (micropapillary and solid) components detection. • Using near-pure radiomic features and patch-wise image analysis, high-grade components of lung adenocarcinoma can be predicted with high sensitivity and moderate specificity. • Using near-pure radiomic features and patch-wise image analysis has potential role in facilitating the prediction of the presence of high-grade components in lung adenocarcinoma prior to surgical resection. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|