Intratumoral and peritumoral radiomics nomograms for the preoperative prediction of lymphovascular invasion and overall survival in non-small cell lung cancer

Clark ME, Bedford LE, Young B et al (2018) Lung cancer CT screening: psychological responses in the presence and absence of pulmonary nodules. Lung Cancer 124:160–167

Article  Google Scholar 

Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A (2012) Global cancer statistics. CA Cancer J Clin 65(2):87–108

Article  Google Scholar 

Ettinger DS, Wood DE, Aisner DL et al (2021) NCCN guidelines insights: non-small cell lung cancer, version 2.2021. J Natl Compr Canc Netw 19(3):254–266

Sung SY, Kwak YK, Lee SW et al (2018) Lymphovascular invasion increases the risk of nodal and distant recurrence in node-negative stage I-iia non-small-cell lung cancer. Oncology 95(3):156–162

Article  Google Scholar 

Okada S, Mizuguchi S, Izumi N et al (2017) Prognostic value of the frequency of vascular invasion in stage I non-small cell lung cancer. Gen Thorac Cardiovasc Surg 65:32–39

Article  Google Scholar 

Shimada Y, Saji H, Kato Y et al (2016) The frequency and prognostic impact of pathological microscopic vascular invasion according to tumor size in non-small cell lung cancer. Chest 149:775–785

Article  Google Scholar 

Ramnefjell M, Aamelfot C, Helgeland L, Akslen LA (2017) Vascular invasion is an adverse prognostic factor in resected non-small-cell lung cancer. APMIS 125(3):197–206

Article  CAS  Google Scholar 

Shimada Y, Saji H, Yoshida K et al (2012) Pathological vascular invasion and tumor differentiation predict cancer recurrence in stage IA non-small-cell lung cancer after complete surgical resection. J Thorac Oncol 7(8):1263–1270

Article  Google Scholar 

Wang S, Xu J, Wang R et al (2018) Adjuvant chemotherapy may improve prognosis after resection of stage I lung cancer with lymphovascular invasion. J Thorac Cardiovasc Surg 156(5):2006–2015.e2

Article  Google Scholar 

Tsutani Y, Miyata Y, Kushitani K, Takeshima Y, Yoshimura M, Okada M (2014) Propensity score-matched analysis of adjuvant chemotherapy for stage I non-small cell lung cancer. J Thorac Cardiovasc Surg 148:1179–1185

Article  Google Scholar 

Yun JK, Lee GD, Choi S et al (2020) Comparison of prognostic impact of lymphovascular invasion in stage IA non-small cell lung cancer after lobectomy versus sublobar resection: a propensity score-matched analysis. Lung Cancer 146:105–111

Article  Google Scholar 

Zhao S, Li F, Guo X et al (2020) New additional scoring formula on the pathological features in stage I lung adenocarcinoma patients: impact on survival. Int J Med Sci 17(13):1871–1878

Article  Google Scholar 

Ito R, Iwano S, Shimamoto H et al (2017) A comparative analysis of dual-phase dual-energy CT and FDG-PET/CT for the prediction of histopathological invasiveness of non-small cell lung cancer. Eur J Radiol 95:186–191

Article  Google Scholar 

Gu Q, Feng Z, Liang Q et al (2019) Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer. Eur J Radiol 118:32–37

Article  Google Scholar 

Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 37(5):1483–1503

Article  Google Scholar 

Yang G, Nie P, Zhao L et al (2020) 2D and 3D texture analysis to predict lymphovascular invasion in lung adenocarcinoma. Eur J Radiol 129:109–111

Google Scholar 

Pietras K, Ostman A (2010) Hallmarks of cancer: interactions with the tumor stroma. Exp Cell Res 316(8):1324–1331

Article  CAS  Google Scholar 

Wang X, Wan Q, Chen H, Li Y, Li X (2020) Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods. Eur Radiol 30(8):4595–4605

Article  Google Scholar 

Dercle L, Fronheiser M, Lu L et al (2020) Identification of non-small cell lung cancer sensitive to systemic cancer therapies using radiomics. Clin Cancer Res 26(9):2151–2162

Article  CAS  Google Scholar 

Giraud P, Antoine M, Larrouy A et al (2000) Evaluation of microscopic tumor extension in non-small-cell lung cancer for three-dimensional conformal radiotherapy planning. Int J Radiat Oncol Biol Phys 48(4):1015–1024

Article  CAS  Google Scholar 

Liu K, Li K, Wu T et al (2022) Improving the accuracy of prognosis for clinical stage I solid lung adenocarcinoma by radiomics models covering tumor per se and peritumoral changes on CT. Eur Radiol 32(2):1065–1077

Article  Google Scholar 

Zhang R, Cai Z, Luo Y, Wang Z, Wang W (2021) Preliminary exploration of response the course of radiotherapy for stage III non-small cell lung cancer based on longitudinal CT radiomics features. Eur J Radiol Open 9:100391

Article  Google Scholar 

Feng Z, Rong P, Cao P et al (2018) Machine learning-based quantitative texture analysis of CT images of small renal masses: differentiation of angiomyolipoma without visible fat from renal cell carcinoma. Eur Radiol 28(4):1625–1633

Article  Google Scholar 

Ren J, Yuan Y, Qi M, Tao X (2020) Machine learning-based CT texture analysis to predict HPV status in oropharyngeal squamous cell carcinoma: comparison of 2D and 3D segmentation. Eur Radiol 30(12):6858–6866

Article  Google Scholar 

Chen Y, Xia Y, Tolat PP et al (2021) Comparison of conventional gadoxetate disodium-enhanced MRI features and radiomics signatures with machine learning for diagnosing microvascular invasion. AJR Am J Roentgenol 216(6):1510–1520

Article  Google Scholar 

Saijo T, Ishii G, Ochiai A et al (2007) Evaluation of extratumoral lymphatic permeation in non-small cell lung cancer as a means of predicting outcome. Lung Cancer 55(1):61–66

Article  Google Scholar 

Pérez-Morales J, Tunali I, Stringfield O et al (2020) Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening. Sci Rep 10(1):10528

Article  CAS  Google Scholar 

Tunali I, Hall LO, Napel S et al (2019) Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions. Med Phys 46(11):5075–5085

Article  Google Scholar 

Joyce JA, Pollard JW et al (2009) Microenvironmental regulation of metastasis. Nat Rev Cancer 9(4):239–252

Article  CAS  Google Scholar 

Jiang T, Song J, Wang X et al (2021) Intratumoral and peritumoral analysis of mammography, tomosynthesis, and multiparametric MRI for predicting Ki-67 level in breast cancer: a radiomics-based study. Mol Imaging Biol. https://doi.org/10.1007/s11307-021-01695-w

Dou TH, Coroller TP, van Griethuysen JJM, Mak RH, Aerts HJWL (2018) Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC. PLoS One 13(11):e0206108

Wu DM, Deng SH, Zhou J et al (2020) PLEK2 mediates metastasis and vascular invasion via the ubiquitin-dependent degradation of SHIP2 in non-small cell lung cancer. Int J Cancer 146(9):2563–2575

Article  CAS  Google Scholar 

Alwithenani A, Bethune D, Castonguay M et al (2021) Profiling non-small cell lung cancer reveals that PD-L1 is associated with wild type EGFR and vascular invasion, and immunohistochemistry quantification of PD-L1 correlates weakly with RT-qPCR. PLoS One 16(5):e0251080

Li C, Tian Y, Shen Y, Wen B, He Y (2021) Utility of volumetric metabolic parameters on preoperative FDG PET/CT for predicting tumor lymphovascular invasion in non-small cell lung cancer. AJR Am J Roentgenol 217(6):1433–1443

Article  Google Scholar 

Shimada Y, Ishii G, Hishida T, Yoshida J, Nishimura M, Nagai K (2010) Extratumoral vascular invasion is a significant prognostic indicator and a predicting factor of distant metastasis in non-small cell lung cancer. J Thorac Oncol 5(7):970–975

Article  Google Scholar 

Sun W, Jiang M, Dang J, Chang P, Yin FF (2018) Effect of machine learning methods on predicting NSCLC overall survival time based on radiomics analysis. Radiat Oncol 13(1):197

Article  Google Scholar 

Botta F, Raimondi S, Rinaldi L et al (2020) Association of a CT-based clinical and radiomics score of non-small cell lung cancer (NSCLC) with lymph node status and overall survival. Cancers (Basel) 12(6):1432

Article  CAS  Google Scholar 

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