{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T14:38:01Z","timestamp":1787495881320,"version":"build-2736575974"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T00:00:00Z","timestamp":1716940800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T00:00:00Z","timestamp":1716940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Chinese Academy of Medical Science Innovation Fund for Medical Sciences","award":["2021-I2M-C&T-B-061"],"award-info":[{"award-number":["2021-I2M-C&T-B-061"]}]},{"name":"Chinese Academy of Medical Science Innovation Fund for Medical Sciences","award":["2022-I2M-C&T-B-076"],"award-info":[{"award-number":["2022-I2M-C&T-B-076"]}]},{"name":"Beijing Hope Run Special Fund of Cancer Foundation of China","award":["LC2022A22"],"award-info":[{"award-number":["LC2022A22"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Respir Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T\n                      <jats:sub>1\u2009\u2212\u20092<\/jats:sub>\n                      N\n                      <jats:sub>0<\/jats:sub>\n                      M\n                      <jats:sub>0<\/jats:sub>\n                      (cT\n                      <jats:sub>1\u2009\u2212\u20092<\/jats:sub>\n                      N\n                      <jats:sub>0<\/jats:sub>\n                      M\n                      <jats:sub>0<\/jats:sub>\n                      ) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT\n                      <jats:sub>1\u2009\u2212\u20092<\/jats:sub>\n                      N\n                      <jats:sub>0<\/jats:sub>\n                      M\n                      <jats:sub>0<\/jats:sub>\n                      SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15\u00a0mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The initial investigation revealed a 33.9% OLM positivity rate in cT\n                      <jats:sub>1\u2009\u2212\u20092<\/jats:sub>\n                      N\n                      <jats:sub>0<\/jats:sub>\n                      M\n                      <jats:sub>0<\/jats:sub>\n                      SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT\n                      <jats:sub>1\u2009\u2212\u20092<\/jats:sub>\n                      N\n                      <jats:sub>0<\/jats:sub>\n                      M\n                      <jats:sub>0<\/jats:sub>\n                      SCLC patients.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT\n                      <jats:sub>1\u2009\u2212\u20092<\/jats:sub>\n                      N\n                      <jats:sub>0<\/jats:sub>\n                      M\n                      <jats:sub>0<\/jats:sub>\n                      SCLC.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12931-024-02852-9","type":"journal-article","created":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T15:01:47Z","timestamp":1716994907000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Incidence rate of occult lymph node metastasis in clinical T1\u22122N0M0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study"],"prefix":"10.1186","volume":"25","author":[{"given":"Xu","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li-Zhi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan-Fen","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng-Wen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Miao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiu-Ming","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia-Liang","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Tang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,29]]},"reference":[{"key":"2852_CR1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209\u201349.","journal-title":"CA Cancer J Clin"},{"key":"2852_CR2","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/S0140-6736(21)00312-3","volume":"398","author":"AA Thai","year":"2021","unstructured":"Thai AA, Solomon BJ, Sequist LV, Gainor JF, Heist RS. Lung cancer. Lancet. 2021;398:535\u201354.","journal-title":"Lancet"},{"key":"2852_CR3","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21708","volume":"72","author":"RL Siegel","year":"2022","unstructured":"Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72:7\u201333.","journal-title":"CA Cancer J Clin"},{"key":"2852_CR4","doi-asserted-by":"crossref","first-page":"s320","DOI":"10.1378\/chest.106.6_Supplement.320S","volume":"106","author":"T Lad","year":"1994","unstructured":"Lad T, Piantadosi S, Thomas P, Payne D, Ruckdeschel J, Giaccone G. A prospective randomized trial to determine the benefit of surgical resection of residual disease following response of small cell lung cancer to combination chemotherapy. Chest. 1994;106:s320\u20133.","journal-title":"Chest"},{"key":"2852_CR5","first-page":"Cd011917","volume":"4","author":"H Barnes","year":"2017","unstructured":"Barnes H, See K, Barnett S, Manser R. Surgery for limited-stage small-cell lung cancer. Cochrane Database Syst Rev. 2017;4:Cd011917.","journal-title":"Cochrane Database Syst Rev"},{"key":"2852_CR6","doi-asserted-by":"crossref","first-page":"e2324","DOI":"10.1097\/MD.0000000000002324","volume":"94","author":"E Miyauchi","year":"2015","unstructured":"Miyauchi E, Motoi N, Ono H, Ninomiya H, Ohyanagi F, Nishio M, Okumura S, Ichinose M, Ishikawa Y. Distinct characteristics of small cell lung cancer correlate with central or peripheral origin: subtyping based on location and expression of transcription factor TTF-1. Med (Baltim). 2015;94:e2324.","journal-title":"Med (Baltim)"},{"key":"2852_CR7","doi-asserted-by":"crossref","first-page":"20150618","DOI":"10.1259\/bjr.20150618","volume":"89","author":"T Nobashi","year":"2016","unstructured":"Nobashi T, Koyasu S, Nakamoto Y, Kubo T, Ishimori T, Kim YH, Yoshizawa A, Togashi K. Prognostic value of fluorine-18 fludeoxyglucose positron emission tomography parameters differs according to primary tumour location in small-cell lung cancer. Br J Radiol. 2016;89:20150618.","journal-title":"Br J Radiol"},{"key":"2852_CR8","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.lungcan.2017.03.013","volume":"108","author":"N Kanaji","year":"2017","unstructured":"Kanaji N, Sakai K, Ueda Y, Miyawaki H, Ishii T, Watanabe N, Kita N, Kadota K, Kadowaki N, Bandoh S. Peripheral-type small cell lung cancer is associated with better survival and higher frequency of interstitial lung disease. Lung Cancer. 2017;108:126\u201333.","journal-title":"Lung Cancer"},{"key":"2852_CR9","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.cllc.2019.07.013","volume":"21","author":"D Uprety","year":"2020","unstructured":"Uprety D, Arjyal L, Vallatharasu Y, Bista A, Borgert A, Fitzsimmons AJ, Parsons BM. Utilization of surgery and its impact on survival in patients with early stage small-cell lung cancer in the United States. Clin Lung Cancer. 2020;21:186\u2013e193182.","journal-title":"Clin Lung Cancer"},{"key":"2852_CR10","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1097\/SLA.0000000000002287","volume":"268","author":"CJ Yang","year":"2018","unstructured":"Yang CJ, Chan DY, Shah SA, Yerokun BA, Wang XF, D\u2019Amico TA, Berry MF, Harpole DHJ. Long-term survival after surgery compared with concurrent chemoradiation for node-negative small cell lung cancer. Ann Surg. 2018;268:1105\u201312.","journal-title":"Ann Surg"},{"key":"2852_CR11","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1200\/JCO.2015.63.8171","volume":"34","author":"CF Yang","year":"2016","unstructured":"Yang CF, Chan DY, Speicher PJ, Gulack BC, Wang X, Hartwig MG, Onaitis MW, Tong BC, D\u2019Amico TA, Berry MF, Harpole DH. Role of adjuvant therapy in a population-based cohort of patients with early-stage small-cell lung cancer. J Clin Oncol. 2016;34:1057\u201364.","journal-title":"J Clin Oncol"},{"key":"2852_CR12","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.6004\/jnccn.2021.0058","volume":"19","author":"AKP Ganti","year":"2021","unstructured":"Ganti AKP, Loo BW, Bassetti M, Blakely C, Chiang A, D\u2019Amico TA, D\u2019Avella C, Dowlati A, Downey RJ, Edelman M, et al. Small cell lung cancer, version 2.2022, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 2021;19:1441\u201364.","journal-title":"J Natl Compr Canc Netw"},{"key":"2852_CR13","doi-asserted-by":"crossref","first-page":"eS400","DOI":"10.1378\/chest.12-2363","volume":"143","author":"JR Jett","year":"2013","unstructured":"Jett JR, Schild SE, Kesler KA, Kalemkerian GP. Treatment of small cell lung cancer: diagnosis and management of lung cancer, 3rd ed: American college of chest physicians evidence-based clinical practice guidelines. Chest. 2013;143:eS400\u201319.","journal-title":"Chest"},{"key":"2852_CR14","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1016\/j.athoracsur.2015.11.056","volume":"101","author":"Y Moon","year":"2016","unstructured":"Moon Y, Kim KS, Lee KY, Sung SW, Kim YK, Park JK. Clinicopathologic factors associated with occult lymph node metastasis in patients with clinically diagnosed no lung adenocarcinoma. Ann Thorac Surg. 2016;101:1928\u201335.","journal-title":"Ann Thorac Surg"},{"key":"2852_CR15","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.lungcan.2013.04.012","volume":"81","author":"L Li","year":"2013","unstructured":"Li L, Ren S, Zhang Y, Guan Y, Zhao J, Liu J, Wang Q, Chen G, Chen H, Xiang J, Fu X. Risk factors for predicting the occult nodal metastasis in T1-2N0M0 NSCLC patients staged by PET\/CT: potential value in the clinic. Lung Cancer. 2013;81:213\u20137.","journal-title":"Lung Cancer"},{"key":"2852_CR16","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1183\/09031936.00126714","volume":"46","author":"G Stamatis","year":"2015","unstructured":"Stamatis G. Staging of lung cancer: the role of noninvasive, minimally invasive and invasive techniques. Eur Respir J. 2015;46:521\u201331.","journal-title":"Eur Respir J"},{"key":"2852_CR17","doi-asserted-by":"crossref","first-page":"1886","DOI":"10.1016\/j.ejrad.2011.03.074","volume":"81","author":"M Li","year":"2012","unstructured":"Li M, Wu N, Liu Y, Zheng R, Liang Y, Zhang W, Zhao P. Regional nodal staging with 18F-FDG PET-CT in non-small cell lung cancer: additional diagnostic value of CT attenuation and dual-time-point imaging. Eur J Radiol. 2012;81:1886\u201390.","journal-title":"Eur J Radiol"},{"key":"2852_CR18","first-page":"Cd009519","volume":"2014","author":"M Schmidt-Hansen","year":"2014","unstructured":"Schmidt-Hansen M, Baldwin DR, Hasler E, Zamora J, Abraira V, Roqu\u00e9 IFM. PET-CT for assessing mediastinal lymph node involvement in patients with suspected resectable non-small cell lung cancer. Cochrane Database Syst Rev. 2014;2014:Cd009519.","journal-title":"Cochrane Database Syst Rev"},{"key":"2852_CR19","doi-asserted-by":"crossref","first-page":"e363","DOI":"10.1016\/j.athoracsur.2017.06.015","volume":"104","author":"H Kayawake","year":"2017","unstructured":"Kayawake H, Chen-Yoshikawa TF, Oda H, Motoyama H, Hamaji M, Menju T, Aoyama A, Sato T, Sonobe M, Date H. Complications of endobronchial ultrasound-guided transbronchial needle aspiration. Ann Thorac Surg. 2017;104:e363\u20135.","journal-title":"Ann Thorac Surg"},{"key":"2852_CR20","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278:563\u201377.","journal-title":"Radiology"},{"key":"2852_CR21","doi-asserted-by":"crossref","first-page":"8157","DOI":"10.2147\/CMAR.S330824","volume":"13","author":"R Zhang","year":"2021","unstructured":"Zhang R, Zhang R, Luan T, Liu B, Zhang Y, Xu Y, Sun X, Xing L. A radiomics nomogram for preoperative prediction of clinical occult lymph node metastasis in cT1-2N0M0 solid lung adenocarcinoma. Cancer Manag Res. 2021;13:8157\u201367.","journal-title":"Cancer Manag Res"},{"key":"2852_CR22","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s12880-020-0416-3","volume":"20","author":"X Sha","year":"2020","unstructured":"Sha X, Gong G, Qiu Q, Duan J, Li D, Yin Y. Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging. BMC Med Imaging. 2020;20:12.","journal-title":"BMC Med Imaging"},{"key":"2852_CR23","doi-asserted-by":"crossref","first-page":"215","DOI":"10.21037\/qims-20-337","volume":"11","author":"L Wang","year":"2021","unstructured":"Wang L, Li T, Hong J, Zhang M, Ouyang M, Zheng X, Tang K. (18)F-FDG PET-based radiomics model for predicting occult lymph node metastasis in clinical N0 solid lung adenocarcinoma. Quant Imaging Med Surg. 2021;11:215\u201325.","journal-title":"Quant Imaging Med Surg"},{"key":"2852_CR24","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s40644-024-00654-2","volume":"24","author":"W Tian","year":"2024","unstructured":"Tian W, Yan Q, Huang X, Feng R, Shan F, Geng D, Zhang Z. Predicting occult lymph node metastasis in solid-predominantly invasive lung adenocarcinoma across multiple centers using radiomics-deep learning fusion model. Cancer Imaging. 2024;24:8.","journal-title":"Cancer Imaging"},{"key":"2852_CR25","doi-asserted-by":"crossref","first-page":"6049","DOI":"10.1007\/s00330-019-06084-0","volume":"29","author":"X Wang","year":"2019","unstructured":"Wang X, Zhao X, Li Q, Xia W, Peng Z, Zhang R, Li Q, Jian J, Wang W, Tang Y, et al. Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT? Eur Radiol. 2019;29:6049\u201358.","journal-title":"Eur Radiol"},{"key":"2852_CR26","doi-asserted-by":"crossref","first-page":"5075","DOI":"10.1002\/mp.13808","volume":"46","author":"I Tunali","year":"2019","unstructured":"Tunali I, Hall LO, Napel S, Cherezov D, Guvenis A, Gillies RJ, Schabath MB. Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions. Med Phys. 2019;46:5075\u201385.","journal-title":"Med Phys"},{"key":"2852_CR27","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/0003-4975(90)90666-T","volume":"50","author":"T Ishida","year":"1990","unstructured":"Ishida T, Yano T, Maeda K, Kaneko S, Tateishi M, Sugimachi K. Strategy for lymphadenectomy in lung cancer three centimeters or less in diameter. Ann Thorac Surg. 1990;50:708\u201313.","journal-title":"Ann Thorac Surg"},{"key":"2852_CR28","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1016\/j.jtcvs.2005.12.035","volume":"131","author":"M Inoue","year":"2006","unstructured":"Inoue M, Minami M, Shiono H, Sawabata N, Ideguchi K, Okumura M. Clinicopathologic study of resected, peripheral, small-sized, non-small cell lung cancer tumors of 2\u00a0cm or less in diameter: pleural invasion and increase of serum carcinoembryonic antigen level as predictors of nodal involvement. J Thorac Cardiovasc Surg. 2006;131:988\u201393.","journal-title":"J Thorac Cardiovasc Surg"},{"key":"2852_CR29","first-page":"1697","volume":"6","author":"F Bao","year":"2014","unstructured":"Bao F, Yuan P, Yuan X, Lv X, Wang Z, Hu J. Predictive risk factors for lymph node metastasis in patients with small size non-small cell lung cancer. J Thorac Dis. 2014;6:1697\u2013703.","journal-title":"J Thorac Dis"},{"key":"2852_CR30","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1097\/JTO.0000000000000630","volume":"10","author":"WD Travis","year":"2015","unstructured":"Travis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JHM, Beasley MB, Chirieac LR, Dacic S, Duhig E, Flieder DB, 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;10:1243\u201360.","journal-title":"J Thorac Oncol"},{"key":"2852_CR31","doi-asserted-by":"crossref","first-page":"138","DOI":"10.3322\/caac.21390","volume":"67","author":"R Rami-Porta","year":"2017","unstructured":"Rami-Porta R, Asamura H, Travis WD, Rusch VW. Lung cancer - major changes in the American joint committee on cancer eighth edition cancer staging manual. CA Cancer J Clin. 2017;67:138\u201355.","journal-title":"CA Cancer J Clin"},{"key":"2852_CR32","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1148\/radiol.2021210109","volume":"302","author":"T Wang","year":"2022","unstructured":"Wang T, She Y, Yang Y, Liu X, Chen S, Zhong Y, Deng J, Zhao M, Sun X, Xie D, Chen C. Radiomics for survival risk stratification of clinical and pathologic stage IA pure-solid non-small cell lung cancer. Radiology. 2022;302:425\u201334.","journal-title":"Radiology"},{"key":"2852_CR33","doi-asserted-by":"crossref","first-page":"100820","DOI":"10.1016\/j.tranon.2020.100820","volume":"13","author":"Y Zhuo","year":"2020","unstructured":"Zhuo Y, Feng M, Yang S, Zhou L, Ge D, Lu S, Liu L, Shan F, Zhang Z. Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma. Transl Oncol. 2020;13:100820.","journal-title":"Transl Oncol"},{"key":"2852_CR34","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1002\/mp.12123","volume":"44","author":"M Shafiq-Ul-Hassan","year":"2017","unstructured":"Shafiq-Ul-Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, Abdalah MA, Schabath MB, Goldgof DG, Mackin D, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys. 2017;44:1050\u201362.","journal-title":"Med Phys"},{"key":"2852_CR35","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJM van Griethuysen","year":"2017","unstructured":"van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin JC, Pieper S, Aerts H. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77:e104\u20137.","journal-title":"Cancer Res"},{"key":"2852_CR36","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s13244-023-01575-7","volume":"15","author":"A Demircio\u011flu","year":"2024","unstructured":"Demircio\u011flu A. The effect of feature normalization methods in radiomics. Insights Imaging. 2024;15:2.","journal-title":"Insights Imaging"},{"key":"2852_CR37","doi-asserted-by":"crossref","first-page":"6414","DOI":"10.1007\/s00330-023-09503-5","volume":"33","author":"P Lin","year":"2023","unstructured":"Lin P, Lin YQ, Gao RZ, Wan WJ, He Y, Yang H. Integrative radiomics and transcriptomics analyses reveal subtype characterization of non-small cell lung cancer. Eur Radiol. 2023;33:6414\u201325.","journal-title":"Eur Radiol"},{"key":"2852_CR38","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1148\/radiol.2019181960","volume":"292","author":"J Choe","year":"2019","unstructured":"Choe J, Lee SM, Do KH, Lee G, Lee JG, Lee SM, Seo JB. Deep learning-based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses. Radiology. 2019;292:365\u201373.","journal-title":"Radiology"},{"key":"2852_CR39","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1177\/0962280217734241","volume":"28","author":"MM Vasquez","year":"2019","unstructured":"Vasquez MM, Hu C, Roe DJ, Halonen M, Guerra S. Measurement error correction in the least absolute shrinkage and selection operator model when validation data are available. Stat Methods Med Res. 2019;28:670\u201380.","journal-title":"Stat Methods Med Res"},{"key":"2852_CR40","doi-asserted-by":"crossref","first-page":"1884","DOI":"10.1007\/s00330-022-09187-3","volume":"33","author":"G Spadarella","year":"2023","unstructured":"Spadarella G, Stanzione A, Akinci D\u2019Antonoli T, Andreychenko A, Fanni SC, Ugga L, Kotter E, Cuocolo R. Systematic review of the radiomics quality score applications: an EuSoMII radiomics auditing group initiative. Eur Radiol. 2023;33:1884\u201394.","journal-title":"Eur Radiol"},{"key":"2852_CR41","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s00259-021-05509-7","volume":"49","author":"Q Chen","year":"2021","unstructured":"Chen Q, Zhang L, Mo X, You J, Chen L, Fang J, Wang F, Jin Z, Zhang B, Zhang S. Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging. 2021;49:345\u201360.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"2852_CR42","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1177\/0272989X06295361","volume":"26","author":"AJ Vickers","year":"2006","unstructured":"Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak. 2006;26:565\u201374.","journal-title":"Med Decis Mak"},{"key":"2852_CR43","doi-asserted-by":"crossref","first-page":"837","DOI":"10.2307\/2531595","volume":"44","author":"ER DeLong","year":"1988","unstructured":"DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837\u201345.","journal-title":"Biometrics"},{"key":"2852_CR44","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1186\/s12885-023-11189-3","volume":"23","author":"JS Cai","year":"2023","unstructured":"Cai JS, Yang F, Wang X. Occult lymph node metastasis is not a favorable factor for resected NSCLC patients. BMC Cancer. 2023;23:822.","journal-title":"BMC Cancer"},{"key":"2852_CR45","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.lungcan.2020.10.022","volume":"150","author":"F Beyaz","year":"2020","unstructured":"Beyaz F, Verhoeven RLJ, Schuurbiers OCJ, Verhagen A, van der Heijden E. Occult lymph node metastases in clinical N0\/N1 NSCLC; a single center in-depth analysis. Lung Cancer. 2020;150:186\u201394.","journal-title":"Lung Cancer"},{"key":"2852_CR46","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1016\/j.athoracsur.2017.12.006","volume":"105","author":"P Gw\u00f3\u017ad\u017a","year":"2018","unstructured":"Gw\u00f3\u017ad\u017a P, Pasieka-Lis M, Ko\u0142odziej K, Pankowski J, Bana\u015b R, Wi\u0142koj\u0107 M, Zieli\u0144ski M. Prognosis of patients with stages I and II non-small cell lung cancer with nodal micrometastases. Ann Thorac Surg. 2018;105:1551\u20137.","journal-title":"Ann Thorac Surg"},{"key":"2852_CR47","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1080\/0284186X.2021.2012253","volume":"61","author":"W Haque","year":"2022","unstructured":"Haque W, Singh A, Park HS, Teh BS, Butler EB, Zeng M, Lin SH, Welsh JW, Chang JY, Verma V. Quantifying the rate and predictors of occult lymph node involvement in patients with clinically node-negative non-small cell lung cancer. Acta Oncol. 2022;61:403\u20138.","journal-title":"Acta Oncol"},{"key":"2852_CR48","doi-asserted-by":"crossref","first-page":"2414","DOI":"10.1007\/s00259-022-05690-3","volume":"49","author":"J Deng","year":"2022","unstructured":"Deng J, Zhong Y, Wang T, Yang M, Ma M, Song Y, She Y, Chen C. Lung cancer with PET\/CT-defined occult nodal metastasis yields favourable prognosis and benefits from adjuvant therapy: a multicentre study. Eur J Nucl Med Mol Imaging. 2022;49:2414\u201324.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"2852_CR49","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1007\/s00268-019-05365-5","volume":"44","author":"Y Moon","year":"2020","unstructured":"Moon Y, Choi SY, Park JK, Lee KY. Risk factors for occult lymph node metastasis in peripheral non-small cell lung cancer with invasive component size 3\u00a0cm or less. World J Surg. 2020;44:1658\u201365.","journal-title":"World J Surg"},{"key":"2852_CR50","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.21037\/jtd-23-1578","volume":"16","author":"MW Liu","year":"2024","unstructured":"Liu MW, Zhang X, Wang YM, Jiang X, Jiang JM, Li M, Zhang L. A comparison of machine learning methods for radiomics modeling in prediction of occult lymph node metastasis in clinical stage IA lung adenocarcinoma patients. J Thorac Dis. 2024;16:1765\u201376.","journal-title":"J Thorac Dis"},{"key":"2852_CR51","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1016\/j.annonc.2021.03.207","volume":"32","author":"AC Dingemans","year":"2021","unstructured":"Dingemans AC, Fr\u00fch M, Ardizzoni A, Besse B, Faivre-Finn C, Hendriks LE, Lantuejoul S, Peters S, Reguart N, Rudin CM, et al. Small-cell lung cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up(\u2606). Ann Oncol. 2021;32:839\u201353.","journal-title":"Ann Oncol"},{"key":"2852_CR52","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1038\/s41572-020-00235-0","volume":"7","author":"CM Rudin","year":"2021","unstructured":"Rudin CM, Brambilla E, Faivre-Finn C, Sage J. Small-cell lung cancer. Nat Rev Dis Primers. 2021;7:3.","journal-title":"Nat Rev Dis Primers"},{"key":"2852_CR53","doi-asserted-by":"crossref","first-page":"109981","DOI":"10.1016\/j.ejrad.2021.109981","volume":"144","author":"XQ He","year":"2021","unstructured":"He XQ, Luo TY, Li X, Huo JW, Gong JW, Li Q. Clinicopathological and computed tomographic features associated with occult lymph node metastasis in patients with peripheral solid non-small cell lung cancer. Eur J Radiol. 2021;144:109981.","journal-title":"Eur J Radiol"},{"key":"2852_CR54","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1093\/ejcts\/ezr272","volume":"42","author":"A G\u00f3mez-Caro","year":"2012","unstructured":"G\u00f3mez-Caro A, Boada M, Caba\u00f1as M, Sanchez M, Arguis P, Lome\u00f1a F, Ramirez J, Molins L. False-negative rate after positron emission tomography\/computer tomography scan for mediastinal staging in cI stage non-small-cell lung cancer. Eur J Cardiothorac Surg. 2012;42:93\u2013100. discussion 100.","journal-title":"Eur J Cardiothorac Surg"},{"key":"2852_CR55","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1093\/neuonc\/noy133","volume":"21","author":"JY Kim","year":"2019","unstructured":"Kim JY, Park JE, Jo Y, Shim WH, Nam SJ, Kim JH, Yoo RE, Choi SH, Kim HS. Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients. Neuro Oncol. 2019;21:404\u201314.","journal-title":"Neuro Oncol"},{"key":"2852_CR56","doi-asserted-by":"crossref","first-page":"109","DOI":"10.2214\/AJR.17.19074","volume":"211","author":"Y Zhong","year":"2018","unstructured":"Zhong Y, Yuan M, Zhang T, Zhang YD, Li H, Yu TF. Radiomics approach to prediction of occult mediastinal lymph node metastasis of lung adenocarcinoma. AJR Am J Roentgenol. 2018;211:109\u201313.","journal-title":"AJR Am J Roentgenol"},{"key":"2852_CR57","doi-asserted-by":"crossref","first-page":"10829","DOI":"10.1038\/s41598-021-90367-4","volume":"11","author":"SK Das","year":"2021","unstructured":"Das SK, Fang KW, Xu L, Li B, Zhang X, Yang HF. Integrative nomogram of intratumoral, peritumoral, and lymph node radiomic features for prediction of lymph node metastasis in cT1N0M0 lung adenocarcinomas. Sci Rep. 2021;11:10829.","journal-title":"Sci Rep"},{"key":"2852_CR58","doi-asserted-by":"crossref","first-page":"5411","DOI":"10.1007\/s00330-023-09513-3","volume":"33","author":"S Zhang","year":"2023","unstructured":"Zhang S, Shao H, Li W, Zhang H, Lin F, Zhang Q, Zhang H, Wang Z, Gao J, Zhang R, et al. Intra- and peritumoral radiomics for predicting malignant BiRADS category 4 breast lesions on contrast-enhanced spectral mammography: a multicenter study. Eur Radiol. 2023;33:5411\u201322.","journal-title":"Eur Radiol"},{"key":"2852_CR59","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1016\/j.jtcvs.2017.12.107","volume":"155","author":"SY Park","year":"2018","unstructured":"Park SY. Nomogram: an analogue tool to deliver digital knowledge. J Thorac Cardiovasc Surg. 2018;155:1793.","journal-title":"J Thorac Cardiovasc Surg"},{"key":"2852_CR60","doi-asserted-by":"crossref","first-page":"e173","DOI":"10.1016\/S1470-2045(14)71116-7","volume":"16","author":"VP Balachandran","year":"2015","unstructured":"Balachandran VP, Gonen M, Smith JJ, DeMatteo RP. Nomograms in oncology: more than meets the eye. Lancet Oncol. 2015;16:e173\u2013180.","journal-title":"Lancet Oncol"},{"key":"2852_CR61","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1016\/j.jtcvs.2020.03.084","volume":"161","author":"K Lim","year":"2021","unstructured":"Lim K, Hsin MKY. Commentary: resection for small cell lung cancer should be offered more often, and preferably anatomical. J Thorac Cardiovasc Surg. 2021;161:1495\u20136.","journal-title":"J Thorac Cardiovasc Surg"},{"key":"2852_CR62","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1111\/j.1440-1843.2010.01843.x","volume":"15","author":"N Navani","year":"2010","unstructured":"Navani N, Spiro SG. PET scanning is important in lung cancer; but it has its limitations. Respirology. 2010;15:1149\u201351.","journal-title":"Respirology"},{"key":"2852_CR63","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s00330-019-06360-z","volume":"30","author":"JE Park","year":"2020","unstructured":"Park JE, Kim D, Kim HS, Park SY, Kim JY, Cho SJ, Shin JH, Kim JH. Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol. 2020;30:523\u201336.","journal-title":"Eur Radiol"},{"key":"2852_CR64","doi-asserted-by":"crossref","unstructured":"Kocak B, Akinci D, Antonoli T, Ates Kus E, Keles A, Kala A, Kose F, Kadioglu M, Solak S, Sunman S, Temiz ZH. Self-reported checklists and quality scoring tools in radiomics: a meta-research. Eur Radiol 2024.","DOI":"10.1007\/s00330-023-10487-5"},{"key":"2852_CR65","unstructured":"Thammasorn P, Wu W, Pierce LA, Pipavath SN, Lampe PD, Houghton AM, Haynor DR, Chaovalitwongse WA, Kinahan P. Deep-learning derived features for lung nodule classification with limited datasets. In Medical Imaging. 2018."},{"key":"2852_CR66","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1109\/TNNLS.2021.3059635","volume":"34","author":"P Thammasorn","year":"2023","unstructured":"Thammasorn P, Chaovalitwongse WA, Hippe DS, Wootton LS, Ford EC, Spraker MB, Combs SE, Peeken JC, Nyflot MJ. Nearest neighbor-based strategy to optimize multi-view triplet network for classification of small-sample medical imaging data. IEEE Trans Neural Netw Learn Syst. 2023;34:586\u2013600.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2852_CR67","doi-asserted-by":"crossref","unstructured":"Zhang L, Li H, Zhao S, Tao X, Li M, Yang S, Zhou L, Liu M, Zhang X, Dong D et al. Deep learning model based on primary tumor to predict lymph node status in clinical stage IA lung adenocarcinoma: a multicenter study. J Natl Cancer Cent 2024.","DOI":"10.1016\/j.jncc.2024.01.005"}],"container-title":["Respiratory Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12931-024-02852-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12931-024-02852-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12931-024-02852-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T18:03:13Z","timestamp":1731520993000},"score":1,"resource":{"primary":{"URL":"https:\/\/respiratory-research.biomedcentral.com\/articles\/10.1186\/s12931-024-02852-9"}},"subtitle":["Original research"],"short-title":[],"issued":{"date-parts":[[2024,5,29]]},"references-count":67,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2852"],"URL":"https:\/\/doi.org\/10.1186\/s12931-024-02852-9","relation":{},"ISSN":["1465-993X"],"issn-type":[{"value":"1465-993X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,29]]},"assertion":[{"value":"3 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was approved by the Cancer Hospital, Chinese Academy of Medical Sciences Ethics Commission (NCC2021C-213).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was waived due to the retrospective nature.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"226"}}