«Previous Article|Table of Contents|Next Article»

Journal Of Modern Oncology[ISSN:1672-4992/CN:61-1415/R]

Issue:

2024 05

Page:

913-920

Research Field:

Publishing date:

Info

Title:

CT radiomics nomogram predicts PD-L1 protein expression in solid non-small cell lung cancer

Author(s):

XU Gang¹; CHEN Peng²; JI Wei³; XIE Zongyu⁴

1.Department of Medical Imaging;3.Department of Respiratory and Critical Care Medicine,Xinhua Hospital of Huainan£¬Anhui University of Science and Technology,Anhui Huainan 232000£¬China£»2.Department of Radiology,Huzhou Central Hospital,Zhejiang Huzhou 313000£¬China£»4.Department of Radiology,the First Affiliated Hospital of Bengbu Medical College£¬Anhui Bengbu 233000£¬China.

Keywords:

non-small cell lung cancer; tomography; X-ray computed; programmed death ligand 1; nomogram; radiomics

PACS:

R734.2

DOI:

10.3969/j.issn.1672-4992.2024.05.022

Abstract:

Objective:To construct nomogram based on computed tomography (CT) feature,and to analyze its value for predicting programmed death ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC).Methods£ºRetrospective analysis was performed on 116 patients with pathologically proven NSCLC with solid tumor manifestations on imaging,including 55 cases of squamous cell carcinoma and 61 cases of adenocarcinoma.Analyzed its clinical and imaging data,screened clinical independent predictive factors,and constructed a clinical model.The images were exported and registered with the plain scan and arterial phase images.The regions of interest of the tumor were sketched manually layer by layer and the radiomics features were extracted.After feature screening,the radiomics model was constructed and the radiomics score was calculated for each patient.The radiomics score and clinical independent predictors were combined to construct a nomogram.The diagnostic performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC).Decision curve analysis (DCA) was used to evaluate the clinical utility of the models,and DeLong's test was used to evaluate the differences between the models.In addition,the two pathological types of squamous cell carcinoma and adenocarcinoma were grouped and subgroup analysis was performed.Results£ºCavity is a clinically independent predictor of PD-L1 expression (with an odds ratio of 3.624).The diagnostic performance of the nomogram (training group AUC vs validation group AUC: 0.861 vs 0.803) was better than that of the radiomics model (AUC: 0.847 vs 0.777) and clinical model (AUC: 0.608 vs 0.570).The decision curve shows that the nomogram has higher clinical application value than the clinical model and the radiomics model.In subgroup analysis,the nomogram still has good diagnostic efficacy,and the diagnostic efficacy of squamous cell carcinoma group is better than that of adenocarcinoma group.Conclusion£ºCT radiomics nomogram can effectively predict the expression status of PD-L1 protein in solid NSCLC tissues before surgery,which can provide help for clinical protocols selection and preoperative decision.

References:

£Û1£ÝBADE BC,DELA CRUZ CS.Lung cancer 2020:Epidemiology,etiology,and prevention £ÛJ£Ý.Clinics in Chest Medicine,2020,41(1):01-24.
£Û2£ÝLI J,GE S,SANG S,et al.Evaluation of PD-L1 expression level in patients with non-small cell lung cancer by (18)F-FDG PET/CT radiomics and clinicopathological characteristics £ÛJ£Ý.Frontiers in Oncology,2021,11:789014.
£Û3£ÝWANG T,SHE Y,YANG Y,et al.Radiomics for survival risk stratification of clinical and pathologic stage Ia pure-solid non-small cell lung cancer £ÛJ£Ý.Radiology,2022,302(2):425-434.
£Û4£ÝHATTORI A,MATSUNAGA T,TAKAMOCHI K,et al.Surgical resection for clinical-stage I radiological pure-solid lung cancer that met the current high risk criteria £ÛJ£Ý.Japanese Aournal of Clinical Oncology,2017,47(7):630-638.

£Û5£ÝHATTORI A,SUZUKI K,TAKAMOCHI K,et al.Prognostic impact of a ground-glass opacity component in clinical stage Ia non-small cell lung cancer £ÛJ£Ý.The Journal of Thoracic and Cardiovascular Surgery,2021,161(4):1469-1480.
£Û6£ÝRIZVI NA,MAZI¦KRES J,PLANCHARD D,et al.Activity and safety of nivolumab,an anti-PD-1 immune checkpoint inhibitor,for patients with advanced,refractory squamous non-small-cell lung cancer (CheckMate 063):A phase 2,single-arm trial £ÛJ£Ý.The Lancet Oncology,2015,16(3):257-265.
£Û7£ÝBORGHAEI H,GETTINGER S,VOKES EE,et al.Five-year outcomes from the randomized,phase ¢ó trials checkmate 017 and 057:Nivolumab versus docetaxel in previously treated non-small-cell lung cancer£ÛJ£Ý.Journal of Clinical Oncology,2021,39(7):723-733.
£Û8£ÝALEXANDER M,KIM SY,CHENG H.Update 2020:Management of non-small cell lung cancer £ÛJ£Ý.Lung,2020,198(6):897-907.
£Û9£ÝLE NQK,KHA QH,NGUYEN VH,et al.Machine learning-based radiomics signatures for EGFR and KRAS mutations prediction in non-small-cell lung cancer £ÛJ£Ý.International Journal of Molecular Sciences,2021,22(17):9254.
£Û10£ÝSAAD MB,HONG L,AMINU M,et al.Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning:A retrospective study £ÛJ£Ý.The Lancet Digital Health,2023,5(7):e404-e420.
£Û11£ÝPARK SY.Nomogram:An analogue tool to deliver digital knowledge £ÛJ£Ý.The Journal of Thoracic and Cardiovascular Surgery,2018,155(4):1793.
£Û12£ÝSUNC,MEZZADRA R,SCHUMACHER TN.Regulation and function of the PD-L1 checkpoint £ÛJ£Ý.Immunity,2018,48(3):434-452.
£Û13£ÝRIBAS A,WOLCHOK JD.Cancer immunotherapy using checkpoint blockade £ÛJ£Ý.Science (New York,NY),2018,359(6382):1350-1355.
£Û14£ÝZHOU YJ,LI G,WANG J,et al.PD-L1:Expression regulation £ÛJ£Ý.Blood Science (Baltimore,Md),2023,5(2):77-91.
£Û15£ÝÍõ¼Ñ»Û,³Â¹«çü.»ùÓÚPD-1/PD-L1ÒÖÖƼÁµÄ·ÇСϸ°û·Î°©ÃâÒßÖÎÁÆÔ¤²â±êÖ¾ÎïµÄÑо¿½øÕ¹£ÛJ£Ý.ÏÖ´úÖ×Áöҽѧ,2021,29£¨10£©:1822-1825. WANG JH,CHEN GY.Advances in the study of predictive markers of immunotherapy for non-small cell lung cancer based on PD-1/PD-L1 inhibitors£ÛJ£Ý.Modern Oncology,2021,29£¨10£©:1822-1825.
£Û16£ÝRECK M,RODR¦PGUEZ-ABREU D,ROBINSON AG,et al.Five-year outcomes with pembrolizumab versus chemotherapy for metastatic non-small-cell lung cancer with PD-L1 tumor proportion score¡Ý50 £ÛJ£Ý.Journal of Clinical Oncology,2021,39(21):2339-2349.
£Û17£ÝHERBST RS,GIACCONE G,DE MARINIS F,et al.Atezolizumab for first-line treatment of PD-L1-selected patients with NSCLC £ÛJ£Ý.The New England Journal of Medicine,2020,383(14):1328-1339.
£Û18£ÝSEZER A,KILICKAP S,G¦aM¦a£ÓM,et al.Cemiplimab monotherapy for first-line treatment of advanced non-small-cell lung cancer with PD-L1 of at least 50%:a multicentre,open-label,global,phase 3,randomised,controlled trial £ÛJ£Ý.Lancet (London,England),2021,397(10274):592-604.
£Û19£ÝGADGEEL S,RODR¦PGUEZ-ABREU D,SPERANZA G,et al.Updated analysis from KEYNOTE-189: Pembrolizumab or placebo plus pemetrexed and platinum for previously untreated metastatic nonsquamous non-small-cell lung cancer £ÛJ£Ý.Journal of Clinical Oncology,2020,38(14):1505-1517.
£Û20£ÝÁõÃ÷¶«,Óà×ÚÑô,ÕÔÖÒÈ«,µÈ.PD-L1¡¢Ki-67Ô¤²â·ÇСϸ°û·Î°©Êõºó½øÕ¹µÄÑо¿£ÛJ£Ý.ÏÖ´úÖ×Áöҽѧ£¬2017£¬25(06):885-889. LIU MD,YU ZY,ZHAO ZQ,et al.To predict postoperative progression by detection of PD-L1£¬Ki-67 in patients with non-small cell lung cancer £ÛJ£Ý.Modern Oncology 2017,25(06):885-889.
£Û21£ÝLAMBIN P,RIOS-VELAZQUEZ E,LEIJENAAR R,et al.Radiomics: Extracting more information from medical images using advanced feature analysis £ÛJ£Ý.European Journal of Cancer (Oxford,England:1990),2012,48(4):441-446.
£Û22£ÝMOON SH,KIM J,JOUNG JG,et al.Correlations between metabolic texture features,genetic heterogeneity,and mutation burden in patients with lung cancer £ÛJ£Ý.European Journal of Nuclear Medicine and Molecular Imaging,2019,46(2):446-454.
£Û23£ÝLIU C,ZHAO W,XIE J,et al.Development and validation of a radiomics-based nomogram for predicting a major pathological response to neoadjuvant immunochemotherapy for patients with potentially resectable non-small cell lung cancer £ÛJ£Ý.Frontiers in Immunology,2023,14:1115291.
£Û24£ÝGAO R,GAO Y,ZHANG J,et al.A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence £ÛJ£Ý.Journal of Cancer Research and Clinical Oncology,2023,149(17):15323-15333.
£Û25£ÝTOYOKAWA G,TAKADA K,OKAMOTO T,et al.Relevance between programmed death ligand 1 and radiologic invasiveness in pathologic stage ¢ñ lung adenocarcinoma£ÛJ£Ý.The Annals of Thoracic Surgery,2017,103(6):1750-1757.
£Û26£ÝSUZUKI K,WATANABE S,MIZUSAWA J,et al.Predictors of non-neoplastic lesions in lung tumours showing ground-glass opacity on thin-section computed tomography based on a multi-institutional prospective study£ÛJ£Ý.Interactive Cardiovascular and Thoracic Surgery,2015,21(2):218-223.
£Û27£ÝWU T,ZHOU F,SOODEEN-LALLOO AK,et al.The association between imaging features of TSCT and the expression of PD-lL in patients with surgical resection of lung adenocarcinoma£ÛJ£Ý.Clinical Lung Cancer,2019,20(2):e195-e207.
£Û28£ÝMU CY,HUANG JA,CHEN Y,et al.High expression of PD-L1 in lung cancer may contribute to poor prognosis and tumor cells immune escape through suppressing tumor infiltrating dendritic cells maturation £ÛJ£Ý.Medical Oncology (Northwood,London,England),2011,28(3):682-688.
£Û29£ÝCUI Y,LI X,DU B,et al.PD-L1 in lung adenocarcinoma: Insights into the role of (18)F-FDG PET/CT £ÛJ£Ý.Cancer Management and Research,2020,12:6385-6395.

Memo

Memo:

°²»ÕÊ¡ÖصãÑо¿Ó뿪·¢¼Æ»®£¨±àºÅ£º2022e07020033£©

Common functions

Last Update: 2024-01-30