Development and Validation of a Nomogram for Predicting the 1-, 3-, and 5-year Survival in Patients with Acinar-predominant Lung Adenocarcinoma

Abstract

Objective

This study aimed to develop a nomogram to predict the overall survival (OS) of patients with acinar-predominant adenocarcinoma (APA).

Methods

Data from patients with APA obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2008 and 2016 were used. Significant prognostic factors were incorporated to construct a nomogram for predicting the 1-, 3-, and 5-year OS in these patients. The discrimination and calibration abilities of the nomogram were assessed using a C-index and calibration curves, respectively.

Results

A total of 2242 patients with APA were randomly divided into a training cohort (n=1576) and validation cohort (n=666). The independent prognostic factors for OS incorporated into the nomogram included marital status, age, gender, differentiation grade, T stage, N stage, and M stage. The nomogram showed good prediction capability, as indicated by the C-index [0.713, 95% confidence interval (CI): 0.705–0.721 in the training cohort, and 0.662, 95% CI: 0.649–0.775 in the validation cohort]. The calibration curves demonstrated that the 1-, 3-, and 5-year OS probabilities were consistent between the observed and predicted outcome frequencies. Patients were divided into the high-risk and low-risk groups with the former showing significantly worse survival than the latter (P<0.001).

Conclusion

Using the SEER database, a nomogram was established to predict the 1-, 3-, and 5-year OS of patients with APA and was superior to the tumor size, lymph node, and metastasis staging system in terms of evaluating long-term prognosis.

Keywords

acinar-predominant adenocarcinoma / nomogram / overall survival / prediction model

References

[1] TravisWD, BrambillaE, NicholsonAG, et al.. The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification. J Thorac Oncol, 2015, 10(9): 1243-1260

[2] LantuejoulS, RouquetteI, BrambillaE, et al.. New WHO classification of lung adenocarcinoma and preneoplasia. Ann Pathol, 2016, 36(1): 5-14

[3] TsaoMS, MarguetS, Le TeuffG, et al.. Subtype Classification of Lung Adenocarcinoma Predicts Benefit From Adjuvant Chemotherapy in Patients Undergoing Complete Resection. J Clin Oncol, 2015, 33(30): 3439-3346

[4] ItoM, MiyataY, TsutaniY, et al.. Positive EGFR mutation status is a risk of recurrence in pN0-1 lung adenocarcinoma when combined with pathological stage and histological subtype: A retrospective multi-center analysis. Lung Cancer, 2020, 141: 107-113

[5] Mansuet-LupoA, BobbioA, BlonsH, et al.. The new histologic classification of lung primary adenocarcinoma subtypes is a reliable prognostic marker and identifies tumors with different mutation status: the experience of a French cohort. Chest, 2014, 146(3): 633-643

[6] YanagawaN, ShionoS, AbikoM, et al.. New IASLC/ATS/ERS classification and invasive tumor size are predictive of disease recurrence in stage I lung adenocarcinoma. J Thorac Oncol, 2013, 8(5): 612-618

[7] LiC, ShenY, HuF, et al.. Micropapillary pattern is associated with the development of brain metastases and the reduction of survival time in EGFR-mutation lung adenocarcinoma patients with surgery. Lung Cancer, 2020, 141: 72-77

[8] MaM, SheY, RenY, et al.. Micropapillary or solid pattern predicts recurrence free survival benefit from adjuvant chemotherapy in patients with stage IB lung adenocarcinoma. J Thorac Dis, 2018, 10(9): 5384-5393

[9] YaldızD, AcarA, Örs KayaŞ, et al.. Papillary predominant histological subtype predicts poor survival in lung adenocarcinoma. Turk Gogus Kalp Damar Cerrahisi Derg, 2019, 27(3): 360-366

[10] YoshizawaA, SumiyoshiS, SonobeM, et al.. Validation of the IASLC/ATS/ERS lung adenocarcinoma classification for prognosis and association with EGFR and KRAS gene mutations: analysis of 440 Japanese patients. J Thorac Oncol, 2013, 8(1): 52-61

[11] ItoH, NakayamaH, Murakami, et al.. Does the histologic predominance of pathological stage IA lung adenocarcinoma influence the extent of resection?. Gen Thorac Cardiovasc Surg, 2017, 65(9): 512-518

[12] ZomboriT, FurákJ, NyáriT, et al.. Evaluation of grading systems in stage I lung adenocarcinomas: a retrospective cohort study. J Clin Pathol, 2018, 71(2): 135-140

[13] MoreiraAL, OcampoPSS, XiaY, et al.. A Grading System for Invasive Pulmonary Adenocarcinoma: A Proposal From the International Association for the Study of Lung Cancer Pathology Committee. J Thorac Oncol, 2020, 15(10): 1599-1610

[14] ItoM, MiyataY, YoshiyaT, et al.. Second predominant subtype predicts outcomes of intermediate-malignant invasive lung adenocarcinoma. Eur J Cardiothorac Surg, 2017, 51(2): 218-222

[15] LiH, WangZ, YangF, et al.. Development and validation of a nomogram for predicting cancer-specific survival of surgical resected stage I–II adenosquamous carcinoma of the lung. J Surg Oncol, 2020, 121(6): 1027-1035

[16] ZengY, MayneN, YangCJ, et al.. A Nomogram for Predicting Cancer-Specific Survival of TNM 8th Edition Stage I Non-small-cell Lung Cancer. Ann Surg Oncol, 2019, 26(7): 2053-2062

[17] IasonosA, SchragD, RajGV, et al.. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol, 2008, 26(8): 1364-1370

[18] ShariatSF, KarakiewiczPI, SuardiN, et al.. Comparison of nomograms with other methods for predicting outcomes in prostate cancer: a critical analysis of the literature. Clin Cancer Res, 2008, 14(14): 4400-4407

[19] HeagertyPJ, LumleyT, PepeMS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics, 2000, 56(2): 337-344

[20] PencinaMJ, D’AgostinoRBSr.. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med, 2008, 27(2): 157-172

[21] VickersAJ, ElkinEB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making, 2006, 26(6): 565-574

[22] MackinnonACJr., LuevanoA, de AraujoLC, et al.. Cribriform adenocarcinoma of the lung: clinicopathologic, immunohistochemical, and molecular analysis of 15 cases of a distinctive morphologic subtype of lung adenocarcinoma. Mod Pathol, 2014, 27(8): 1063-1072

[23] KimM, ChungYS, KimKA, et al.. Prognostic factors of acinar- or papillary-predominant adenocarcinoma of the lung. Lung Cancer, 2019, 137: 129-135

[24] LuD, YangJ, LiuX, et al.. Clinicopathological features, survival outcomes, and appropriate surgical approaches for stage I acinar and papillary predominant lung adenocarcinoma. Cancer Med, 2020, 9(10): 3455-3462

[25] SuzukiK, SajiH, AokageK, et al.. Comparison of pulmonary segmentectomy and lobectomy: Safety results of a randomized trial. J Thorac Cardiovasc Surg, 2019, 158(3): 895-907

[26] WenZ, ZhaoY, FuF, et al.. Comparison of outcomes following segmentectomy or lobectomy for patients with clinical N0 invasive lung adenocarcinoma of 2 cm or less in diameter. J Cancer Res Clin Oncol, 2020, 146(6): 1603-1613

[27] CaoS, TengJ, XuJ, et al.. Value of adjuvant chemotherapy in patients with resected stage IB solid predominant and solid non-predominant lung adenocarcinoma. Thorac Cancer, 2019, 10(2): 249-255

[28] WangC, YangJ, LuM. Micropapillary Predominant Lung Adenocarcinoma in Stage IA Benefits from Adjuvant Chemotherapy. Ann Surg Oncol, 2020, 27(6): 2051-2060