Clinical Stage III NSCLC Patients Treated with Neoadjuvant Therapy and Surgery: The Prognostic Role of Nodal Characteristics

Chiappetta, Marco; Tabacco, Diomira; Iaffaldano, Amedeo; Evangelista, Jessica; Congedo, Maria Teresa; Sassorossi, Carolina; Meacci, Elisa; D’Argento, Ettore; Bria, Emilio; Vita, Emanuele; Boldrini, Luca; Charles-Davies, Diepriye; Massaccesi, Mariangela; Martino, Antonella; Mazzarella, Ciro; Valentini, Vincenzo; Margaritora, Stefano; Lococo, Filippo

doi:10.3390/life12111753

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…The late diagnosis of NSCLC results in a great deal of heterogeneity in terms of prognosis and treatment as patients may be eligible for surgery, chemotherapy, radiotherapy, or a combination of these treatments. Modern therapies have been able to achieve unprecedented survival rates, with increasing numbers of patients experiencing long-term remission [4][5][6][7]. However, this success comes at a cost, with numerous side effects associated with these therapies, including radiation-induced toxicity to the heart and coronary vessels [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

CARdioimaging in Lung Cancer PatiEnts Undergoing Radical RadioTherapy: CARE-RT Trial

Nardone,

Belfiore,

De Chiara

et al. 2023

Diagnostics

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Background: Non-small-cell lung cancer (NSCLC) is a common, steady growing lung tumour that is often discovered when a surgical approach is forbidden. For locally advanced inoperable NSCLC, the clinical approach consists of a combination of chemotherapy and radiotherapy, eventually followed by adjuvant immunotherapy, a treatment that is useful but may cause several mild and severe adverse effect. Chest radiotherapy, specifically, may affect the heart and coronary artery, impairing heart function and causing pathologic changes in myocardial tissues. The aim of this study is to evaluate the damage coming from these therapies with the aid of cardiac imaging. Methods: This is a single-centre, prospective clinical trial. Patients with NSCLC who are enrolled will undergo computed tomography (CT) and magnetic resonance imaging (MRI) before chemotherapy 3 months, 6 months, and 9–12 months after the treatment. We expect to enrol 30 patients in 2 years. Conclusions: Our clinical trial will be an opportunity not only to highlight the timing and the radiation dose needed for pathological cardiac tissue changes to happen but will also provide useful data to set new follow-up schedules and strategies, keeping in mind that, more often than not, patients affected by NSCLC may present other heart- and lung-related pathological conditions.

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Section: Introductionmentioning

confidence: 99%

CARdioimaging in Lung Cancer PatiEnts Undergoing Radical RadioTherapy: CARE-RT Trial

Nardone,

Belfiore,

De Chiara

et al. 2023

Diagnostics

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“…In this setting, surgery is a fundamental part of this multidisciplinary management, offering the opportunity to definitely eradicate the disease and providing valuable information on the final pathological stage. Indeed, a complete response may be used as a surrogate marker for survival outcome, and information about the final nodal status, such as persistent N2 disease or lymph node ratio, may better define the prognosis and lead to adjuvant treatments [ 20 ].…”

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confidence: 99%

Surgery for Non-Small Cell Lung Cancer in the Personalized Therapy Era

Chiappetta,

Sassorossi,

Cusumano

2023

Current Oncology

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Development and validation of machine learning models to predict survival of patients with resected stage-III NSCLC

Jin

Zhao

Fu³

et al. 2023

Front. Oncol.

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ObjectiveTo compare the performance of three machine learning algorithms with the tumor, node, and metastasis (TNM) staging system in survival prediction and validate the individual adjuvant treatment recommendations plan based on the optimal model.MethodsIn this study, we trained three machine learning madel and validated 3 machine learning survival models-deep learning neural network, random forest and cox proportional hazard model- using the data of patients with stage-al3 NSCLC patients who received resection surgery from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) database from 2012 to 2017,the performance of survival predication from all machine learning models were assessed using a concordance index (c-index) and the averaged c-index is utilized for cross-validation. The optimal model was externally validated in an independent cohort from Shaanxi Provincial People’s Hospital. Then we compare the performance of the optimal model and TNM staging system. Finally, we developed a Cloud-based recommendation system for adjuvant therapy to visualize survival curve of each treatment plan and deployed on the internet.ResultsA total of 4617 patients were included in this study. The deep learning network performed more stably and accurately in predicting stage-iii NSCLC resected patients survival than the random survival forest and Cox proportional hazard model on the internal test dataset (C-index=0.834 vs. 0.678 vs. 0.640) and better than TNM staging system (C-index=0.820 vs. 0.650) in the external validation. The individual patient who follow the reference from recommendation system had superior survival compared to those who did not. The predicted 5-year-survival curve for each adjuvant treatment plan could be accessed in the recommender system via the browser.ConclusionDeep learning model has several advantages over linear model and random forest model in prognostic predication and treatment recommendations. This novel analytical approach may provide accurate predication on individual survival and treatment recommendations for resected Stage-iii NSCLC patients.

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Clinical Stage III NSCLC Patients Treated with Neoadjuvant Therapy and Surgery: The Prognostic Role of Nodal Characteristics

Cited by 3 publications

References 32 publications

CARdioimaging in Lung Cancer PatiEnts Undergoing Radical RadioTherapy: CARE-RT Trial

CARdioimaging in Lung Cancer PatiEnts Undergoing Radical RadioTherapy: CARE-RT Trial

Surgery for Non-Small Cell Lung Cancer in the Personalized Therapy Era

Development and validation of machine learning models to predict survival of patients with resected stage-III NSCLC

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