|
1
|
Hendriks LEL, Remon J, Faivre-Finn C,
Garassino MC, Heymach JV, Kerr KM, Tan DSW, Veronesi G and Reck M:
Non-small-cell lung cancer. Nat Rev Dis Primers. 10:712024.
View Article : Google Scholar
|
|
2
|
Rathore K, Weightman W, Palmer K, Hird K
and Joshi P: Survival analysis of early-stage NSCLC patients
following lobectomy: Impact of surgical techniques and other
variables on long-term outcomes. Heart Lung Circ. 34:639–646. 2025.
View Article : Google Scholar
|
|
3
|
Lou F, Sima CS, Rusch VW, Jones DR and
Huang J: Differences in patterns of recurrence in early-stage
versus locally advanced non-small cell lung cancer. Ann Thorac
Surg. 98:1755–1761. 2014. View Article : Google Scholar
|
|
4
|
Xie H, Su H, Zhu E, Gu C, Zhao S, She Y,
Ren Y, Xie D, Zheng H, Wu C, et al: Morphological subtypes of tumor
spread through air spaces in non-small cell lung cancer: Prognostic
heterogeneity and its underlying mechanism. Front Oncol.
11:6083532021. View Article : Google Scholar
|
|
5
|
Han YB, Kim H, Mino-Kenudson M, Cho S,
Kwon HJ, Lee KR, Kwon S, Lee J, Kim K, Jheon S, et al: Tumor spread
through air spaces (STAS): Prognostic significance of grading in
non-small cell lung cancer. Mod Pathol. 34:549–561. 2021.
View Article : Google Scholar
|
|
6
|
Kutlay C, Gülhan SŞE, Acar LN, Aslan M and
Tanrıkulu FB: Impact of spread through air spaces (STAS) and
lymphovascular invasion (LVI) on prognosis in NSCLC: A
comprehensive pathological evaluation. Updates Surg. 77:1205–1213.
2025. View Article : Google Scholar
|
|
7
|
Nicholson AG, Tsao MS, Beasley MB, Borczuk
AC, Brambilla E, Cooper WA, Dacic S, Jain D, Kerr KM, Lantuejoul S,
et al: The 2021 WHO classification of lung tumors: Impact of
advances since 2015. J Thorac Oncol. 17:362–387. 2022. View Article : Google Scholar
|
|
8
|
Liu C, Meng A, Xue XQ, Wang YF, Jia C, Yao
DP, Wu YJ, Huang Q, Gong P and Li XF: Prediction of early lung
adenocarcinoma spread through air spaces by machine learning
radiomics: A cross-center cohort study. Transl Lung Cancer Res.
13:3443–3459. 2024. View Article : Google Scholar
|
|
9
|
Wang X, Ma C, Jiang Q, Zheng X, Xie J, He
C, Gu P, Wu Y, Xiao Y and Liu S: Performance of deep learning model
and radiomics model for preoperative prediction of spread through
air spaces in the surgically resected lung adenocarcinoma: A
two-center comparative study. Transl Lung Cancer Res. 13:3486–3499.
2024. View Article : Google Scholar
|
|
10
|
Swanson K, Wu E, Zhang A, Alizadeh AA and
Zou J: From patterns to patients: Advances in clinical machine
learning for cancer diagnosis, prognosis, and treatment. Cell.
186:1772–1791. 2023. View Article : Google Scholar
|
|
11
|
Zhang B, Shi H and Wang H: Machine
learning and AI in cancer prognosis, prediction, and treatment
selection: A critical approach. J Multidiscip Healthc.
16:1779–1791. 2023. View Article : Google Scholar
|
|
12
|
Zou Y, Mao Q, Zhao Z, Zhou X, Pan Y, Zuo Z
and Zhang W: Intratumoural and peritumoural CT-based radiomics for
diagnosing lepidic-predominant adenocarcinoma in patients with pure
ground-glass nodules: A machine learning approach. Clin Radiol.
79:e211–e218. 2024. View Article : Google Scholar
|
|
13
|
Parisineni SRA and Pal M: Enhancing trust
and interpretability of complex machine learning models using local
interpretable model agnostic shap explanations. Int J Data Sci
Anal. 18:457–466. 2023. View Article : Google Scholar
|
|
14
|
Li Y, Ding J, Wu K, Qi W, Lin S, Chen G
and Zuo Z: Ensemble machine learning classifiers combining CT
radiomics and clinical-radiological features for preoperative
prediction of pathological invasiveness in lung adenocarcinoma
presenting as part-solid nodules: A multicenter retrospective
study. Technol Cancer Res Treat. 24:153303382513513652025.
View Article : Google Scholar
|
|
15
|
Fong KM, Rosenthal A, Giroux DJ, Nishimura
KK, Erasmus J, Lievens Y, Marino M, Marom EM, Putora PM, Singh N,
et al: The international association for the study of lung cancer
staging project for lung cancer: Proposals for the revision of the
M descriptors in the forthcoming ninth edition of the TNM
classification for lung cancer. J Thorac Oncol. 19:786–802. 2024.
View Article : Google Scholar
|
|
16
|
Zuo Z, Wang P, Zeng W, Qi W and Zhang W:
Measuring pure ground-glass nodules on computed tomography:
Assessing agreement between a commercially available deep learning
algorithm and radiologists' readings. Acta Radiol. 64:1422–1430.
2023. View Article : Google Scholar
|
|
17
|
Dunn C, Brettle D, Cockroft M, Keating E,
Revie C and Treanor D: Quantitative assessment of H&E staining
for pathology: Development and clinical evaluation of a novel
system. Diagn Pathol. 19:422024. View Article : Google Scholar
|
|
18
|
Christodoulou E, Ma J, Collins GS,
Steyerberg EW, Verbakel JY and van Calster B: A systematic review
shows no performance benefit of machine learning over logistic
regression for clinical prediction models. J Clin Epidemiol.
110:12–22. 2019. View Article : Google Scholar
|
|
19
|
Venkat V, Clark K, Jeng XJ, Yao TC, Tsai
HJ, Lu TP, Hsiao TH, Lin CH, Holloway S, Hoyo C, et al: Exploring
random forest in genetic risk score construction. Genet Epidemiol.
49:e700222025. View Article : Google Scholar
|
|
20
|
Kim KS, Yoon TJ, Ahn J and Ryu JA:
Development and validation of a machine learning model for early
prediction of acute kidney injury in neurocritical care: A
comparative analysis of XGBoost, GBM, and random forest algorithms.
Diagnostics (Basel). 15:20612025. View Article : Google Scholar
|
|
21
|
Byvatov E and Schneider G: Support vector
machine applications in bioinformatics. Appl Bioinformatics.
2:67–77. 2003.
|
|
22
|
Zhang J, Hao L, Xu Q and Gao F: Radiomics
and clinical characters based gaussian naive bayes (GNB) model for
preoperative differentiation of pulmonary pure invasive mucinous
adenocarcinoma from mixed mucinous adenocarcinoma. Technol Cancer
Res Treat. 23:153303382412584152024. View Article : Google Scholar
|
|
23
|
Yang W, Jiang J, Schnellinger EM, Kimmel
SE and Guo W: Modified Brier score for evaluating prediction
accuracy for binary outcomes. Stat Methods Med Res. 31:2287–2296.
2022. View Article : Google Scholar
|
|
24
|
Ling T, Zuo Z, Huang M, Wu L, Ma J, Huang
X and Tang W: Prediction of mucinous adenocarcinoma in colorectal
cancer with mucinous components detected in preoperative biopsy
diagnosis. Abdom Radiol (NY). 50:2794–2805. 2025. View Article : Google Scholar
|
|
25
|
Ling T, Zuo Z, Huang M, Ma J and Wu L:
Stacking classifiers based on integrated machine learning model:
Fusion of CT radiomics and clinical biomarkers to predict lymph
node metastasis in locally advanced gastric cancer patients after
neoadjuvant chemotherapy. BMC Cancer. 25:8342025. View Article : Google Scholar
|
|
26
|
Zhou Y, Zhao J, Zou F, Tan Y, Zeng W,
Jiang J, Hu J, Zeng Q, Gong L, Liu L and Zhong L: Interpretable
machine learning models based on body composition and inflammatory
nutritional index (BCINI) to predict early postoperative recurrence
of colorectal cancer: Multi-center study. Comput Methods Programs
Biomed. 269:1088742025. View Article : Google Scholar
|
|
27
|
Yang M, Chen Y, Zhou X, Yu R, Huang N and
Chen J: Machine learning models for prediction of NPVR ≥80% with
HIFU ablation for uterine fibroids. Int J Hyperthermia.
42:24737542025. View Article : Google Scholar
|
|
28
|
Lü X, Wang C, Tang M, Li J, Xia Z, Fan S,
Jin Y and Yang Z: Pinpointing potent hits for cancer immunotherapy
targeting the TIGIT/PVR pathway using the XGBoost model,
centroid-based virtual screening, and MD simulation. Comput Biol
Chem. 118:1084502025. View Article : Google Scholar
|
|
29
|
Chen S, Wang X, Lin X, Li Q, Xu S, Sun H,
Xiao Y, Fan L and Liu S: CT-based radiomics predictive model for
spread through air space of IA stage lung adenocarcinoma. Acta
Radiol. 66:477–486. 2025. View Article : Google Scholar
|
|
30
|
Wang S, Xu M, Liu Y, Hou X, Gao Z, Sun J
and Shen L: PD-L1 expression and its association with
clinicopathological and computed tomography features in surgically
resected non-small cell lung cancer: A retrospective cohort study.
Sci Rep. 15:243232025. View Article : Google Scholar
|
|
31
|
Wang P, Cui J, Du H, Qian Z, Zhan H, Zhang
H, Ye W, Meng W and Bai R: Preoperative prediction of STAS risk in
primary lung adenocarcinoma using machine learning: An
interpretable model with SHAP analysis. Acad Radiol. 32:4266–4277.
2025. View Article : Google Scholar
|
|
32
|
Su Y, Tao J, Lan X, Liang C, Huang X,
Zhang J, Li K and Chen L: CT-based intratumoral and peritumoral
radiomics nomogram to predict spread through air spaces in lung
adenocarcinoma with diameter ≤ 3 cm: A multicenter study. Eur J
Radiol Open. 14:1006302025. View Article : Google Scholar
|
|
33
|
Xie M, Gao J, Ma X, Wu C, Zang X, Wang Y,
Deng H, Yao J, Sun T, Yu Z, et al: Consolidation radiographic
morphology can be an indicator of the pathological basis and
prognosis of partially solid nodules. BMC Pulm Med. 22:3692022.
View Article : Google Scholar
|
|
34
|
Zhang X, Qiao W, Shen J, Jiang Q, Pan C,
Wang Y, Bidzińska J, Dai F and Zhang L: Clinical, pathological, and
computed tomography morphological features of lung cancer with
spread through air spaces. Transl Lung Cancer Res. 13:2802–2812.
2024. View Article : Google Scholar
|
|
35
|
Wang Y, Lyu D, Zhang D, Hu L, Wu J, Tu W,
Xiao Y, Fan L and Liu S: Nomogram based on clinical characteristics
and radiological features for the preoperative prediction of spread
through air spaces in patients with clinical stage IA non-small
cell lung cancer: A multicenter study. Diagn Interv Radiol.
29:771–785. 2023. View Article : Google Scholar
|
|
36
|
Liu X, Ding Y, Ren J, Li J, Wang K, Sun S,
Zhang W, Xu M, Jing Y, Gao G, et al: Analysis of factors affecting
the diagnostic efficacy of frozen sections for tumor spread through
air spaces in lung adenocarcinoma. Cancers (Basel). 17:21682025.
View Article : Google Scholar
|
|
37
|
Qi L, Li X, He L, Cheng G, Cai Y, Xue K
and Li M: Comparison of diagnostic performance of spread through
airspaces of lung adenocarcinoma based on morphological analysis
and perinodular and intranodular radiomic features on chest CT
images. Front Oncol. 11:6544132021. View Article : Google Scholar
|
|
38
|
Warth A, Muley T, Kossakowski CA, Goeppert
B, Schirmacher P, Dienemann H and Weichert W: Prognostic impact of
intra-alveolar tumor spread in pulmonary adenocarcinoma. Am J Surg
Pathol. 39:793–801. 2015. View Article : Google Scholar
|
|
39
|
Shi J, Xu K, Liu X, Shi M, Ji C and Ye B:
Anaplastic lymphoma kinase rearrangement and tumor spread through
air spaces is associated with worse clinical outcomes for resected
stage IA lung adenocarcinoma. Clin Lung Cancer.
S1525-7304(25)00222-0. 2025.(Epub ahead of print). View Article : Google Scholar
|
|
40
|
Huang G, Wang L, Zhao Z, Wang Y, Li B,
Huang Z, Yu X, Liang N and Li S: Development and internal
validation of predictive models for spread through air spaces in
clinical stage IA lung adenocarcinoma. Gen Thorac Cardiovasc Surg.
Apr 28–2025.(Epub ahead of print). View Article : Google Scholar
|
|
41
|
Fu H, Liu K, Zheng Y, Zhao J, Xie T and
Ding Y: Upregulation of ARHGAP18 by miR-613 inhibits cigarette
smoke extract-induced apoptosis and epithelial-mesenchymal
transition in bronchial epithelial cells. Int J Chron Obstruct
Pulmon Dis. 20:2525–2537. 2025. View Article : Google Scholar
|
|
42
|
Díaz-Gay M, Zhang T, Hoang PH, Leduc C,
Baine MK, Travis WD, Sholl LM, Joubert P, Khandekar A, Zhao W, et
al: The mutagenic forces shaping the genomes of lung cancer in
never smokers. Nature. 644:133–144. 2025. View Article : Google Scholar
|
|
43
|
Zhang W, Chen B, Zhao C, Yang D, Shima M,
Fan W, Yoda Y, Li S, Guo C, Chen Y, et al: Personal exposure to
PM2.5 and O3 induced heterogeneous
inflammatory responses and modifying effects of smoking: A
prospective panel study in COPD patients. J Hazard Mater.
494:1384712025. View Article : Google Scholar
|
|
44
|
Wang J, Yao Y, Tang D and Gao W: An
individualized nomogram for predicting and validating spread
through air space (STAS) in surgically resected lung
adenocarcinoma: A single center retrospective analysis. J
Cardiothorac Surg. 18:3372023. View Article : Google Scholar
|
|
45
|
Yang Y, Li L, Hu H, Zhou C, Huang Q, Zhao
J, Duan Y, Li W, Luo J, Jiang J, et al: A nomogram integrating the
clinical and CT imaging characteristics for assessing spread
through air spaces in clinical stage IA lung adenocarcinoma. Front
Immunol. 16:15197662025. View Article : Google Scholar
|
|
46
|
van den Heuvel M, Holdenrieder S,
Schuurbiers M, Cigoianu D, Trulson I, van Rossum H and Lang D:
Serum tumor markers for response prediction and monitoring of
advanced lung cancer: A review focusing on immunotherapy and
targeted therapies. Tumour Biol. 46((S1)): S233–S268. 2023.
|