Predicting pleural invasion of invasive lung adenocarcinoma in the adjacent pleura by imaging histology

Kong,Lingxin; Xue,Wenfei; Zhao,Huanfen; Zhang,Xiaopeng; Chen,Shuangqing; Ren,Dahu; Duan,Guochen

doi:10.3892/ol.2023.14025

Predicting pleural invasion of invasive lung adenocarcinoma in the adjacent pleura by imaging histology

Authors:
- Lingxin Kong
- Wenfei Xue
- Huanfen Zhao
- Xiaopeng Zhang
- Shuangqing Chen
- Dahu Ren
- Guochen Duan
View Affiliations

Affiliations: Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, Hebei 050000, P.R. China, Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, Hebei 050000, P.R. China, Department of Pathology, Hebei General Hospital, Shijiazhuang, Hebei 050000, P.R. China, Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, Hebei 050000, P.R. China
Published online on: August 23, 2023 https://doi.org/10.3892/ol.2023.14025
Article Number: 438
Copyright: © Kong et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

The aim of the present study was to develop a non‑invasive method based on histological imaging and clinical features for predicting the preoperative status of visceral pleural invasion (VPI) in patients with lung adenocarcinoma (LUAD) located near the pleura. VPI is associated with a worse prognosis of LUAD; therefore, early and accurate detection is critical for effective treatment planning. A total of 112 patients with preoperative computed tomography presentation of adjacent pleura and postoperative pathological findings confirmed as invasive LUAD were retrospectively enrolled. Clinical and histological imaging features were combined to develop a preoperative VPI prediction model and validate the model's efficacy. Finally, a nomogram for predicting LUAD was established and validated using a logistic regression algorithm. Both the clinical signature and radiomics signature (Rad signature) exhibited a perfect fit in the training cohort. The clinical signature was overfitted in the testing cohort, whereas the Rad signature showed a good fit. To combine clinical and radiomics signatures for optimal performance, a nomogram was created using the logistic regression algorithm. The results indicated that this approach had the highest predictive performance, with an area under the curve of 0.957 for the clinical signature and 0.900 for the Rad signature. In conclusion, histological imaging and clinical features can be combined in columnar maps to predict the preoperative VPI status of patients with adjacent pleural infiltrative lung carcinoma.

View Figures

View References

1	Siegel RL, Miller KD and Jemal A: Cancer Statistics, 2017. CA Cancer J Clin. 67:7–30. 2017. View Article : Google Scholar : PubMed/NCBI
2	Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A and Bray F: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 71:209–249. 2021. View Article : Google Scholar : PubMed/NCBI
3	Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, Nicholson AG, Groome P, Mitchell A, Bolejack V, et al: The IASLC lung cancer staging project: Proposals for revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM classification for lung cancer. J Thorac Oncol. 11:39–51. 2016. View Article : Google Scholar : PubMed/NCBI
4	Mizuno T, Arimura T, Kuroda H, Sakakura N, Yatabe Y and Sakao Y: P2.16-34 visceral pleural invasion is closely associated with nodal spread in cStage IA lung adenocarcinoma. J Thorac Oncol. 13:S8452018. View Article : Google Scholar
5	Hwang JH, Song KS, Park SI, Lim TH, Kwon KH and Goo DE: Subtle pleural metastasis without large effusion in lung cancer patients: Preoperative detection on CT. Korean J Radiol. 6:94–101. 2005. View Article : Google Scholar : PubMed/NCBI
6	Ahn SY, Park CM, Jeon YK, Kim H, Lee JH, Hwang EJ and Goo JM: Predictive CT features of visceral pleural invasion by T1-Sized peripheral pulmonary adenocarcinomas manifesting as subsolid nodules. AJR Am J Roentgenol. 209:561–566. 2017. View Article : Google Scholar : PubMed/NCBI
7	Takizawa H, Kondo K, Kawakita N, Tsuboi M, Toba H, Kajiura K, Kawakami Y, Sakiyama S, Tangoku A, Morishita A, et al: Autofluorescence for the diagnosis of visceral pleural invasion in non-small-cell lung cancer. Eur J Cardiothorac Surg. 53:987–992. 2018. View Article : Google Scholar : PubMed/NCBI
8	McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M and Clark GM; Statistics Subcommittee of the NCI-EORTC Working Group on Cancer Diagnostics, : Reporting recommendations for tumor MARKer prognostic studies (REMARK). Nat Clin Pract Urol. 2:416–422. 2005. View Article : Google Scholar : PubMed/NCBI
9	Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC and Gerig G: User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage. 31:1116–1128. 2006. View Article : Google Scholar : PubMed/NCBI
10	Jung G, Hwang HS, Jang SJ and Ro JY: Are elastic stain and specialty sign out necessary to evaluate pleural invasion in lung cancers? Ann Diagn Pathol. 16:250–254. 2012. View Article : Google Scholar : PubMed/NCBI
11	Hsu JS, Han IT, Tsai TH, Lin SF, Jaw TS, Liu GC, Chou SH, Chong IW and Chen CY: Pleural tags on CT scans to predict visceral pleural invasion of non-small cell lung cancer that does not abut the pleura. Radiology. 279:590–596. 2016. View Article : Google Scholar : PubMed/NCBI
12	Imai K, Minamiya Y, Ishiyama K, Hashimoto M, Saito H, Motoyama S, Sato Y and Ogawa J: Use of CT to evaluate pleural invasion in non-small cell lung cancer: Measurement of the ratio of the interface between tumor and neighboring structures to maximum tumor diameter. Radiology. 267:619–626. 2013. View Article : Google Scholar : PubMed/NCBI
13	Yang S, Yang L, Teng L, Zhang S, Cui Y, Cao Y and Shi H: Visceral pleural invasion by pulmonary adenocarcinoma ≤3 cm: The pathological correlation with pleural signs on computed tomography. J Thorac Dis. 10:3992–3999. 2018. View Article : Google Scholar : PubMed/NCBI
14	Travis WD, Brambilla E, Rami-Porta R, Vallières E, Tsuboi M, Rusch V and Goldstraw P; International Staging Committee. Visceral pleural invasion, : Pathologic criteria and use of elastic stains: Proposal for the 7th edition of the TNM classification for lung cancer. J Thorac Oncol. 3:1384–1390. 2008. View Article : Google Scholar : PubMed/NCBI
15	Wang T, Zhou C and Zhou Q: Extent of visceral pleural invasion affects prognosis of resected non-small cell lung cancer: A meta-analysis. Sci Rep. 7:15272017. View Article : Google Scholar : PubMed/NCBI
16	Liang RB, Li P, Li BT, Jin JT, Rusch VW, Jones DR, Wu YL, Liu Q, Yang J, Yang MZ, et al: Modification of pathologic T classification for non-small cell lung cancer with visceral pleural invasion: Data from 1,055 cases of cancers ≤3 cm. Chest. 160:754–764. 2021. View Article : Google Scholar : PubMed/NCBI
17	Zhao Q, Wang JW, Yang L, Xue LY and Lu WW: CT diagnosis of pleural and stromal invasion in malignant subpleural pure ground-glass nodules: An exploratory study. Eur Radiol. 29:279–286. 2019. View Article : Google Scholar : PubMed/NCBI
18	Zuo Z, Li Y, Peng K, Li X, Tan Q, Mo Y, Lan Y, Zeng W and Qi W: CT texture analysis-based nomogram for the preoperative prediction of visceral pleural invasion in cT1N0M0 lung adenocarcinoma: An external validation cohort study. Clin Radiol. 77:e215–e221. 2022. View Article : Google Scholar : PubMed/NCBI
19	Iizuka S, Kawase A, Oiwa H, Ema T, Shiiya N and Funai K: A risk scoring system for predicting visceral pleural invasion in non-small lung cancer patients. Gen Thorac Cardiovasc Surg. 67:876–879. 2019. View Article : Google Scholar : PubMed/NCBI
20	Kim H, Goo JM, Kim YT and Park CM: CT-defined visceral pleural invasion in T1 lung adenocarcinoma: Lack of relationship to disease-free survival. Radiology. 292:741–749. 2019. View Article : Google Scholar : PubMed/NCBI
21	Choi H, Kim H, Hong W, Park J, Hwang EJ, Park CM, Kim YT and Goo JM: Prediction of visceral pleural invasion in lung cancer on CT: Deep learning model achieves a radiologist-level performance with adaptive sensitivity and specificity to clinical needs. Eur Radiol. 31:2866–2876. 2021. View Article : Google Scholar : PubMed/NCBI
22	Klinkenberg TJ, Dinjens L, Wolf RFE, van der Wekken AJ, van de Wauwer C, de Bock GH, Timens W, Mariani MA and Groen HJM: CT-guided percutaneous hookwire localization increases the efficacy and safety of VATS for pulmonary nodules. J Surg Oncol. 115:898–904. 2017. View Article : Google Scholar : PubMed/NCBI