Lobectomy or Sublobar Resection? Evidence-Based and AI-Enhanced Strategies for Early-Stage Non-Small Cell Lung Cancer
Main Article Content
Abstract
Background. Lobectomy has historically been the gold standard surgical treatment for early-stage non-small cell lung cancer (NSCLC). However, the role of sublobar resections, particularly segmentectomy, has been redefined over the past decade, with recent randomized trials challenging traditional paradigms. In parallel, artificial intelligence (AI) is emerging as a transformative tool in thoracic surgery, offering new opportunities for preoperative risk stratification, surgical planning, and individualized treatment selection.
This review synthesizes current evidence comparing sublobar resections and lobectomy in early-stage NSCLC, examining both oncologic and functional outcomes, and explores the potential of AI and real-world data to refine surgical decision-making.
Methods. A narrative review of key randomized controlled trials, meta-analyses, and recent literature on AI in thoracic oncology was performed, emphasizing patient selection, risk assessment, and integration of AI-derived tools.
Results. Trials such as JCOG0802/WJOG4607L and CALGB 140503 demonstrate non-inferior survival with anatomical segmentectomy for selected tumors ≤2 cm, though higher locoregional recurrence rates in specific subgroups underscore the importance of patient selection. Functional preservation after segmentectomy is modest and not universally superior to lobectomy. AI applications"ranging from radiomics-based tumor characterization to machine learning"driven risk prediction"offer promising avenues to support nuanced surgical decisions, but prospective validation and clinical integration remain limited.
Conclusions. Surgical decision-making for early-stage NSCLC is evolving from a one-size-fits-all model toward precision strategies integrating oncologic, functional, and patient-specific factors. AI has the potential to enhance this process by providing individualized prognostic and functional predictions. Future research should focus on prospective evaluation of AI-assisted decision pathways, addressing barriers to adoption, and aligning surgical strategies with personalized cancer care.
Keywords:
Artificial intelligence, segmentectomy, lobectomy, lung cancer, surgical decision-making, personalized medicine
Article Details
How to Cite
BRIVIO, Margherita et al.
Lobectomy or Sublobar Resection? Evidence-Based and AI-Enhanced Strategies for Early-Stage Non-Small Cell Lung Cancer.
Medical Research Archives, [S.l.], v. 13, n. 12, dec. 2025.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/7135>. Date accessed: 02 jan. 2026.
doi: https://doi.org/10.18103/mra.v13i12.7135.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
1. Jensik RJ. Segmental resection for lung cancer. A fifteen-year experience. J Thorac Cardiovasc Surg. 1973;66(4):563-572.
2. Akinci D’Antonoli T, Farchione A, Lenkowicz J, et al. CT Radiomics Signature of Tumor and Peritumoral Lung Parenchyma to Predict Nonsmall Cell Lung Cancer Postsurgical Recurrence Risk. Academic Radiology. 2020;27(4):497-507. doi:10.1016/j.acra.2019.05.019
3. Rakaee M, Andersen S, Giannikou K, et al. Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial. Annals of Oncology. 2023;34(7):578-588. doi:10.1016/j.annonc.2023.04.005
4. Li Q, Wang X, Liang F, et al. A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images. Biostatistics. 2019;20(4):565-581. doi:10.1093/biostatistics/kxy019
5. Loftus TJ, Tighe PJ, Filiberto AC, et al. Artificial Intelligence and Surgical Decision-making. JAMA Surg. 2020;155(2):148. doi:10.1001/jamasurg.2019.4917
6. Rubinstein LV. Randomized Trial of Lobectomy Versus Limited Resection for T1 NO N o n - S m a l l Cell Lung Cancer.
7. Bertolaccini L, Tralongo AC, Del Re M, et al. Segmentectomy vs. Lobectomy in stage IA non-small cell lung cancer: A systematic review and meta-analysis of perioperative and survival outcomes. Lung Cancer. 2024;197:107990. doi:10.1016/j.lungcan.2024.107990
8. Suzuki K, Saji H, Aokage K, et al. Comparison of pulmonary segmentectomy and lobectomy: Safety results of a randomized trial. The Journal of Thoracic and Cardiovascular Surgery. 2019;158(3):895-907. doi:10.1016/j.jtcvs.2019.03.090
9. Altorki N, Wang X, Kozono D, et al. Lobar or Sublobar Resection for Peripheral Stage IA Non–Small-Cell Lung Cancer. N Engl J Med. 2023;388(6):489-498. doi:10.1056/NEJMoa2212083
10. Altorki N, Wang X, Damman B, et al. Lobectomy, segmentectomy, or wedge resection for peripheral clinical T1aN0 non–small cell lung cancer: A post hoc analysis of CALGB 140503 (Alliance). The Journal of Thoracic and Cardiovascular Surgery. 2024;167(1): 338-347.e1. doi:10.1016/j.jtcvs.2023.07.008
11. Bertolaccini L, Spaggiari L. Is It Time to Cross the Pillars of Evidence in Favor of Segmentectomies in Early-Stage Non-Small Cell Lung Cancer? Cancers. 2023;15(7):1993. doi:10.3390/cancers15071993
12. Hattori A, Suzuki K, Takamochi K, et al. Segmentectomy versus lobectomy in small-sized peripheral non-small-cell lung cancer with radiologically pure-solid appearance in Japan (JCOG0802/WJOG4607L): a post-hoc supplemental analysis of a multicentre, open-label, phase 3 trial. The Lancet Respiratory Medicine. 2024;12(2):105-116. doi:10.1016/S2213-2600 (23)00382-X
13. Brunelli A, Kim AW, Berger KI, Addrizzo-Harris DJ. Physiologic Evaluation of the Patient With Lung Cancer Being Considered for Resectional Surgery. Chest. 2013;143(5):e166S-e190S. doi:10.1378/chest.12-2395
14. Mazzone P. Preoperative evaluation of the lung resection candidate. CCJM. 2012;79(5 e-suppl 1):S17-S22. doi:10.3949/ccjm.79.s2.04
15. Lim E, Baldwin D, Beckles M, et al. Guidelines on the radical management of patients with lung cancer. Thorax. 2010;65(Suppl 3):iii1-iii27. doi:10.1136/thx.2010.145938
16. Brunelli A, Charloux A, Bolliger CT, et al. ERS/ESTS clinical guidelines on fitness for radical therapy in lung cancer patients (surgery and chemo-radiotherapy). Eur Respir J. 2009;34(1):17-41. doi:10.1183/09031936.00184308
17. Xu Y, Qin Y, Ma D, Liu H. The impact of segmentectomy versus lobectomy on pulmonary function in patients with non-small-cell lung cancer: a meta-analysis. J Cardiothorac Surg. 2022;17(1):107. doi:10.1186/s13019-022-01853-3
18. Fang W. Commentary: Is segmentectomy ready to be accepted as the standard of care? The Journal of Thoracic and Cardiovascular Surgery. 2021;161(1): 292-293. doi:10.1016/j.jtcvs.2020.01.037
19. Yang D, Miao Y, Liu C, et al. Advances in artificial intelligence applications in the field of lung cancer. Front Oncol. 2024;14:1449068. doi:10.3389/fonc.2024.1449068
20. Mohseni A, Ghotbi E, Kazemi F, et al. Artificial Intelligence in Radiology. Radiologic Clinics of North America. 2024;62(6):935-947. doi:10.1016/j.rcl.2024.03.008
21. Cellina M, Cacioppa LM, Cè M, et al. Artificial Intelligence in Lung Cancer Screening: The Future Is Now. Cancers. 2023;15(17):4344. doi:10.3390/cancers15174344
22. Kim RY, Oke JL, Pickup LC, et al. Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT. Radiology. 2022;304(3):683-691. doi:10.1148/radiol.212182
23. Cellina M, Cè M, Irmici G, et al. Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future. Diagnostics. 2022;12(11):2644. doi:10.3390/diagnostics12112644
24. Yanagawa M. Artificial Intelligence Improves Radiologist Performance for Predicting Malignancy at Chest CT. Radiology. 2022;304(3):692-693. doi:10.1148/radiol.220571
25. Atasever S, Azginoglu N, Terzi DS, Terzi R. A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning. Clinical Imaging. 2023;94:18-41. doi:10.1016/j.clinimag.2022.11.003
26. Yun J, Park J, Yu D, et al. Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net. Medical Image Analysis. 2019;51:13-20. doi:10.1016/j.media.2018.10.006
27. Kwon OB, Lee HU, Park HE, et al. Predicting Postoperative Lung Function in Patients with Lung Cancer Using Imaging Biomarkers. Diseases. 2024;12(4):65. doi:10.3390/diseases12040065
28. Li X, Zhang S, Luo X, et al. Accuracy and efficiency of an artificial intelligence-based pulmonary broncho-vascular three-dimensional reconstruction system supporting thoracic surgery: retrospective and prospective validation study. eBioMedicine. 2023;87:104422. doi:10.1016/j.ebiom.2022.104422
29. Lv Y, Wei Y, Xu K, et al. 3D deep learning versus the current methods for predicting tumor invasiveness of lung adenocarcinoma based on high-resolution computed tomography images. Front Oncol. 2022;12:995870. doi:10.3389/fonc.2022.995870
30. Yoshiyasu N, Kojima F, Hayashi K, Bando T. Radiomics technology for identifying early-stage lung adenocarcinomas suitable for sublobar resection. The Journal of Thoracic and Cardiovascular Surgery. 2021;162(2):477-485.e1. doi:10.1016/j.jtcvs.2020.05.009
31. Brunelli A, Salati M, Rocco G, et al. European risk models for morbidity (EuroLung1) and mortality (EuroLung2) to predict outcome following anatomic lung resections: an analysis from the European Society of Thoracic Surgeons database†, ‡. Eur J Cardiothorac Surg. Published online October 15, 2016:ezw319. doi:10.1093/ejcts/ezw319
32. Brunelli A, Charloux A, Bolliger CT, et al. ERS/ESTS clinical guidelines on fitness for radical therapy in lung cancer patients (surgery and chemo-radiotherapy). Eur Respir J. 2009;34(1):17-41. doi:10.1183/09031936.00184308
33. Bellini V, Valente M, Del Rio P, Bignami E. Artificial intelligence in thoracic surgery: a narrative review. J Thorac Dis. 2021;13(12):6963-6975. doi:10.21037/jtd-21-761
34. Etienne H, Hamdi S, Le Roux M, et al. Artificial intelligence in thoracic surgery: past, present, perspective and limits. Eur Respir Rev. 2020;29(157):200010. doi:10.1183/16000617.0010-2020
35. Esteva H, Marchevsky A, Núñez T, Luna C, Esteva M. Neural networks as a prognostic tool of surgical risk in lung resections. The Annals of Thoracic Surgery. 2002;73(5):1576-1581. doi:10.1016/S0003-4975(02)03418-5
36. Santos-Garcı́a G, Varela G, Novoa N, Jiménez MF. Prediction of postoperative morbidity after lung resection using an artificial neural network ensemble. Artificial Intelligence in Medicine. 2004;30(1):61-69. doi:10.1016/S0933-3657(03)00059-9
37. Salati M, Migliorelli L, Moccia S, et al. A Machine Learning Approach for Postoperative Outcome Prediction: Surgical Data Science Application in a Thoracic Surgery Setting. World j surg. 2021;45(5):1585-1594. doi:10.1007/s00268-020-05948-7
38. Chang YJ, Hung KC, Wang LK, et al. A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery. IJERPH. 2021;18(5):2713. doi:10.3390/ijerph18052713
39. Topalovic M, Das N, Janssens W. Artificial intelligence for pulmonary function test interpretation. Eur Respir J. 2019;53(6):1900782. doi:10.1183/13993003.00782-2019
40. Birkhoff DC, Van Dalen ASHM, Schijven MP. A Review on the Current Applications of Artificial Intelligence in the Operating Room. Surg Innov. 2021;28(5):611-619. doi:10.1177/1553350621996961
41. Mack MJ. Minimally Invasive and Robotic Surgery. JAMA. 2001;285(5):568. doi:10.1001/jama.285.5.568
42. Dai Y, Abiri A, Pensa J, et al. Biaxial sensing suture breakage warning system for robotic surgery. Biomed Microdevices. 2019;21(1):10. doi:10.1007/s10544-018-0357-6
43. Cho Y, Lee A, Park J, Ko B, Kim N. Enhancement of gesture recognition for contactless interface using a personalized classifier in the operating room. Computer Methods and Programs in Biomedicine. 2018;161:39-44. doi:10.1016/j.cmpb.2018.04.003
44. Fard MJ, Ameri S, Darin Ellis R, Chinnam RB, Pandya AK, Klein MD. Automated robot‐assisted surgical skill evaluation: Predictive analytics approach. Robotics Computer Surgery. 2018;14(1):e1850. doi:10.1002/rcs.1850
2. Akinci D’Antonoli T, Farchione A, Lenkowicz J, et al. CT Radiomics Signature of Tumor and Peritumoral Lung Parenchyma to Predict Nonsmall Cell Lung Cancer Postsurgical Recurrence Risk. Academic Radiology. 2020;27(4):497-507. doi:10.1016/j.acra.2019.05.019
3. Rakaee M, Andersen S, Giannikou K, et al. Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial. Annals of Oncology. 2023;34(7):578-588. doi:10.1016/j.annonc.2023.04.005
4. Li Q, Wang X, Liang F, et al. A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images. Biostatistics. 2019;20(4):565-581. doi:10.1093/biostatistics/kxy019
5. Loftus TJ, Tighe PJ, Filiberto AC, et al. Artificial Intelligence and Surgical Decision-making. JAMA Surg. 2020;155(2):148. doi:10.1001/jamasurg.2019.4917
6. Rubinstein LV. Randomized Trial of Lobectomy Versus Limited Resection for T1 NO N o n - S m a l l Cell Lung Cancer.
7. Bertolaccini L, Tralongo AC, Del Re M, et al. Segmentectomy vs. Lobectomy in stage IA non-small cell lung cancer: A systematic review and meta-analysis of perioperative and survival outcomes. Lung Cancer. 2024;197:107990. doi:10.1016/j.lungcan.2024.107990
8. Suzuki K, Saji H, Aokage K, et al. Comparison of pulmonary segmentectomy and lobectomy: Safety results of a randomized trial. The Journal of Thoracic and Cardiovascular Surgery. 2019;158(3):895-907. doi:10.1016/j.jtcvs.2019.03.090
9. Altorki N, Wang X, Kozono D, et al. Lobar or Sublobar Resection for Peripheral Stage IA Non–Small-Cell Lung Cancer. N Engl J Med. 2023;388(6):489-498. doi:10.1056/NEJMoa2212083
10. Altorki N, Wang X, Damman B, et al. Lobectomy, segmentectomy, or wedge resection for peripheral clinical T1aN0 non–small cell lung cancer: A post hoc analysis of CALGB 140503 (Alliance). The Journal of Thoracic and Cardiovascular Surgery. 2024;167(1): 338-347.e1. doi:10.1016/j.jtcvs.2023.07.008
11. Bertolaccini L, Spaggiari L. Is It Time to Cross the Pillars of Evidence in Favor of Segmentectomies in Early-Stage Non-Small Cell Lung Cancer? Cancers. 2023;15(7):1993. doi:10.3390/cancers15071993
12. Hattori A, Suzuki K, Takamochi K, et al. Segmentectomy versus lobectomy in small-sized peripheral non-small-cell lung cancer with radiologically pure-solid appearance in Japan (JCOG0802/WJOG4607L): a post-hoc supplemental analysis of a multicentre, open-label, phase 3 trial. The Lancet Respiratory Medicine. 2024;12(2):105-116. doi:10.1016/S2213-2600 (23)00382-X
13. Brunelli A, Kim AW, Berger KI, Addrizzo-Harris DJ. Physiologic Evaluation of the Patient With Lung Cancer Being Considered for Resectional Surgery. Chest. 2013;143(5):e166S-e190S. doi:10.1378/chest.12-2395
14. Mazzone P. Preoperative evaluation of the lung resection candidate. CCJM. 2012;79(5 e-suppl 1):S17-S22. doi:10.3949/ccjm.79.s2.04
15. Lim E, Baldwin D, Beckles M, et al. Guidelines on the radical management of patients with lung cancer. Thorax. 2010;65(Suppl 3):iii1-iii27. doi:10.1136/thx.2010.145938
16. Brunelli A, Charloux A, Bolliger CT, et al. ERS/ESTS clinical guidelines on fitness for radical therapy in lung cancer patients (surgery and chemo-radiotherapy). Eur Respir J. 2009;34(1):17-41. doi:10.1183/09031936.00184308
17. Xu Y, Qin Y, Ma D, Liu H. The impact of segmentectomy versus lobectomy on pulmonary function in patients with non-small-cell lung cancer: a meta-analysis. J Cardiothorac Surg. 2022;17(1):107. doi:10.1186/s13019-022-01853-3
18. Fang W. Commentary: Is segmentectomy ready to be accepted as the standard of care? The Journal of Thoracic and Cardiovascular Surgery. 2021;161(1): 292-293. doi:10.1016/j.jtcvs.2020.01.037
19. Yang D, Miao Y, Liu C, et al. Advances in artificial intelligence applications in the field of lung cancer. Front Oncol. 2024;14:1449068. doi:10.3389/fonc.2024.1449068
20. Mohseni A, Ghotbi E, Kazemi F, et al. Artificial Intelligence in Radiology. Radiologic Clinics of North America. 2024;62(6):935-947. doi:10.1016/j.rcl.2024.03.008
21. Cellina M, Cacioppa LM, Cè M, et al. Artificial Intelligence in Lung Cancer Screening: The Future Is Now. Cancers. 2023;15(17):4344. doi:10.3390/cancers15174344
22. Kim RY, Oke JL, Pickup LC, et al. Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT. Radiology. 2022;304(3):683-691. doi:10.1148/radiol.212182
23. Cellina M, Cè M, Irmici G, et al. Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future. Diagnostics. 2022;12(11):2644. doi:10.3390/diagnostics12112644
24. Yanagawa M. Artificial Intelligence Improves Radiologist Performance for Predicting Malignancy at Chest CT. Radiology. 2022;304(3):692-693. doi:10.1148/radiol.220571
25. Atasever S, Azginoglu N, Terzi DS, Terzi R. A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning. Clinical Imaging. 2023;94:18-41. doi:10.1016/j.clinimag.2022.11.003
26. Yun J, Park J, Yu D, et al. Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net. Medical Image Analysis. 2019;51:13-20. doi:10.1016/j.media.2018.10.006
27. Kwon OB, Lee HU, Park HE, et al. Predicting Postoperative Lung Function in Patients with Lung Cancer Using Imaging Biomarkers. Diseases. 2024;12(4):65. doi:10.3390/diseases12040065
28. Li X, Zhang S, Luo X, et al. Accuracy and efficiency of an artificial intelligence-based pulmonary broncho-vascular three-dimensional reconstruction system supporting thoracic surgery: retrospective and prospective validation study. eBioMedicine. 2023;87:104422. doi:10.1016/j.ebiom.2022.104422
29. Lv Y, Wei Y, Xu K, et al. 3D deep learning versus the current methods for predicting tumor invasiveness of lung adenocarcinoma based on high-resolution computed tomography images. Front Oncol. 2022;12:995870. doi:10.3389/fonc.2022.995870
30. Yoshiyasu N, Kojima F, Hayashi K, Bando T. Radiomics technology for identifying early-stage lung adenocarcinomas suitable for sublobar resection. The Journal of Thoracic and Cardiovascular Surgery. 2021;162(2):477-485.e1. doi:10.1016/j.jtcvs.2020.05.009
31. Brunelli A, Salati M, Rocco G, et al. European risk models for morbidity (EuroLung1) and mortality (EuroLung2) to predict outcome following anatomic lung resections: an analysis from the European Society of Thoracic Surgeons database†, ‡. Eur J Cardiothorac Surg. Published online October 15, 2016:ezw319. doi:10.1093/ejcts/ezw319
32. Brunelli A, Charloux A, Bolliger CT, et al. ERS/ESTS clinical guidelines on fitness for radical therapy in lung cancer patients (surgery and chemo-radiotherapy). Eur Respir J. 2009;34(1):17-41. doi:10.1183/09031936.00184308
33. Bellini V, Valente M, Del Rio P, Bignami E. Artificial intelligence in thoracic surgery: a narrative review. J Thorac Dis. 2021;13(12):6963-6975. doi:10.21037/jtd-21-761
34. Etienne H, Hamdi S, Le Roux M, et al. Artificial intelligence in thoracic surgery: past, present, perspective and limits. Eur Respir Rev. 2020;29(157):200010. doi:10.1183/16000617.0010-2020
35. Esteva H, Marchevsky A, Núñez T, Luna C, Esteva M. Neural networks as a prognostic tool of surgical risk in lung resections. The Annals of Thoracic Surgery. 2002;73(5):1576-1581. doi:10.1016/S0003-4975(02)03418-5
36. Santos-Garcı́a G, Varela G, Novoa N, Jiménez MF. Prediction of postoperative morbidity after lung resection using an artificial neural network ensemble. Artificial Intelligence in Medicine. 2004;30(1):61-69. doi:10.1016/S0933-3657(03)00059-9
37. Salati M, Migliorelli L, Moccia S, et al. A Machine Learning Approach for Postoperative Outcome Prediction: Surgical Data Science Application in a Thoracic Surgery Setting. World j surg. 2021;45(5):1585-1594. doi:10.1007/s00268-020-05948-7
38. Chang YJ, Hung KC, Wang LK, et al. A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery. IJERPH. 2021;18(5):2713. doi:10.3390/ijerph18052713
39. Topalovic M, Das N, Janssens W. Artificial intelligence for pulmonary function test interpretation. Eur Respir J. 2019;53(6):1900782. doi:10.1183/13993003.00782-2019
40. Birkhoff DC, Van Dalen ASHM, Schijven MP. A Review on the Current Applications of Artificial Intelligence in the Operating Room. Surg Innov. 2021;28(5):611-619. doi:10.1177/1553350621996961
41. Mack MJ. Minimally Invasive and Robotic Surgery. JAMA. 2001;285(5):568. doi:10.1001/jama.285.5.568
42. Dai Y, Abiri A, Pensa J, et al. Biaxial sensing suture breakage warning system for robotic surgery. Biomed Microdevices. 2019;21(1):10. doi:10.1007/s10544-018-0357-6
43. Cho Y, Lee A, Park J, Ko B, Kim N. Enhancement of gesture recognition for contactless interface using a personalized classifier in the operating room. Computer Methods and Programs in Biomedicine. 2018;161:39-44. doi:10.1016/j.cmpb.2018.04.003
44. Fard MJ, Ameri S, Darin Ellis R, Chinnam RB, Pandya AK, Klein MD. Automated robot‐assisted surgical skill evaluation: Predictive analytics approach. Robotics Computer Surgery. 2018;14(1):e1850. doi:10.1002/rcs.1850