Risk models to predict outcomes following lung cancer surgery: where are we at?
Editorial Commentary

Risk models to predict outcomes following lung cancer surgery: where are we at?

Elena Prisciandaro1, Luca Bertolaccini1, Antonio Mazzella1, Giulia Sedda2, Lorenzo Spaggiari1,2

1Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Milan, Italy; 2Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy

Correspondence to: Luca Bertolaccini, MD, PhD, FCCP. Division of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy. Email: luca.bertolaccini@gmail.com.

Received: 08 August 2020; Accepted: 27 August 2020; Published: 25 May 2022.

doi: 10.21037/ccts-20-138

Quality of care optimization is essential in modern healthcare environments and particularly in elective cancer surgical settings, as adverse events are frequent among patients who undergo invasive procedures.

This is particularly true today, as the evolution of anaesthetic and surgical techniques is pushing the boundaries of operability, and an increasing number of elderlies with a wide variety of comorbidities can benefit from minimally invasive operations. Besides, the advances of targeted therapies are expanding the range of treatment options, proving to be valuable alternatives to surgery.

Therefore, with the advent of personalized medicine and tailored therapies, several predictive models have been developed to support surgical decision-making, thus offering surgical candidates the most appropriate treatment.

According to the latest National Institute for Health and Care Excellence (NICE) guidelines for the diagnosis and management of lung cancer, the preoperative assessment of elective surgical patients should include a global risk scoring system to estimate the risk of death (1). In this regard, a prominent example is the European Society Objective Score (2), that allows us to determine the risk of in-hospital death following lung resection. Similarly, the Thoracoscore (3) helps to predict 30-day mortality after thoracic surgery and has received both internal and external validation (4). The reproducibility of these models, however, is unclear, as several studies have reported inadequate performances when they are employed in different geographical populations (5-7).

More recently, Brunelli and colleagues (8) elaborated two prediction models for risk-adjusting morbidity (EuroLung1) and mortality (EuroLung2), based on an extensive database of the European Society of Thoracic Surgery, including almost 48,000 patients undergoing anatomical pulmonary resection. They proved to be reliable tools for internal audit of performance when employed in three different European centres (9). However, in a Japanese single-centre analysis (10), the EuroLung scores overestimated both morbidity and mortality and could not be directly applied to the study population.

Similarly, Chinese researchers reported promising results with a novel nomogram prognostic model forecasting lung cancer-related death rate, another cancer-related death rate, and non-cancer-related death rate after pulmonary resection (11).

Lately, the emerging landscape of immune therapies has prompted researchers to construct lung cancer immune-related prognostic indices, that showed high accuracy in survival forecasting, particularly for the squamous cell histotypes (11,12).

In conclusion, prognostic risk scores are being increasingly used in thoracic surgery, especially in patients undergoing pulmonary resection. They are valuable tools for improving patient quality of care and designing personalized treatment strategies. However, such complex models need to be established on rigorous methodology and both internal and external validation.


This work was partially supported by the Italian Ministry of Health with Ricerca Corrente and 5x1000 funds.

Funding: None.


Provenance and Peer Review: This article was commissioned by the Guest Editors (Davide Tosi and Alessandro Palleschi) for the series “The Treatment of Locally Advanced Lung Cancer” published in Current Challenges in Thoracic Surgery. The article did not undergo external peer review.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://ccts.amegroups.com/article/view/10.21037/ccts-20-138/coif). The series “The Treatment of Locally Advanced Lung Cancer” was commissioned by the editorial office without any funding or sponsorship. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of this work in ensuring that questions related to the accuracy or integrity of any part of this work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.


  1. Lung Cancer: Diagnosis and Management. NICE Guideline, 2019. Available online: https://www.nice.org.uk/guidance/ng122
  2. Berrisford R, Brunelli A, Rocco G, et al. The European Thoracic Surgery Database project: modelling the risk of in-hospital death following lung resection. Eur J Cardiothorac Surg 2005;28:306-11. [Crossref] [PubMed]
  3. Falcoz PE, Conti M, Brouchet L, et al. The Thoracic Surgery Scoring System (Thoracoscore): risk model for in-hospital death in 15,183 patients requiring thoracic surgery. J Thorac Cardiovasc Surg 2007;133:325-32. [Crossref] [PubMed]
  4. Die Loucou J, Pagès PB, Falcoz PE, et al. Validation and update of the thoracic surgery scoring system (Thoracoscore) risk model. Eur J Cardiothorac Surg 2020;58:350-6. [Crossref] [PubMed]
  5. Sharkey A, Ariyaratnam P, Anikin V, et al. Thoracoscore and European Society Objective Score Fail to Predict Mortality in the UK. World J Oncol 2015;6:270-5. [Crossref] [PubMed]
  6. Bradley A, Marshall A, Abdelaziz M, et al. Thoracoscore fails to predict complications following elective lung resection. Eur Respir J 2012;40:1496-501. [Crossref] [PubMed]
  7. Barua A, Handagala SD, Socci L, et al. Accuracy of two scoring systems for risk stratification in thoracic surgery. Interact Cardiovasc Thorac Surg 2012;14:556-9. [Crossref] [PubMed]
  8. 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 2017;51:490-7. Erratum in: Eur J Cardiothorac Surg 2017 Jun 1;51(6):1212. [PubMed]
  9. Pompili C, Shargall Y, Decaluwe H, et al. Risk-adjusted performance evaluation in three academic thoracic surgery units using the Eurolung risk models. Eur J Cardiothorac Surg 2018;54:122-6. [Crossref] [PubMed]
  10. Nagoya A, Kanzaki R, Kanou T, et al. Validation of Eurolung risk models in a Japanese population: a retrospective single-centre analysis of 612 cases. Interact Cardiovasc Thorac Surg 2019;29:722-8. [Crossref] [PubMed]
  11. Liu Z, Wan Y, Qiu Y, et al. Development and validation of a novel immune-related prognostic model in lung squamous cell carcinoma. Int J Med Sci 2020;17:1393-405. [Crossref] [PubMed]
  12. Zhang J, Zhang J, Yuan C, et al. Establishment of the prognostic index of lung squamous cell carcinoma based on immunogenomic landscape analysis. Cancer Cell Int 2020;20:330. [Crossref] [PubMed]
doi: 10.21037/ccts-20-138
Cite this article as: Prisciandaro E, Bertolaccini L, Mazzella A, Sedda G, Spaggiari L. Risk models to predict outcomes following lung cancer surgery: where are we at? Curr Chall Thorac Surg 2022;4:12.

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