Composite anatomical-clinical-molecular prognostic model in nonsmall cell lung cancer

López-Encuentra, Ángel; López‐Ríos, Fernando; Conde, Esther; García-Luján, Ricardo; Suárez-Gauthier, Ana; Manes, Nicla; Renedo, G; Duque-Medina, José Luis; García-Lagarto, Elena; Rami‐Porta, Ramón; González-Pont, Guadalupe; Astudillo-Pombo, J.; Matesanz, J L; Freixinet, J.; Romero-Saavedra, T.; Sanchez-Cespedes, Montse; Cámara, Agustı́n Gómez de la

doi:10.1183/09031936.00028610

Cited by 14 publications

(10 citation statements)

References 28 publications

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“…This work leverages the research on successful predictive modeling in NSCLC that has been previously established by several researchers [14], [15], [38]. In this paper, we demonstrate the development of a decision support model for patient selection in NSCLC using realworld patient data.…”

Section: Discussionmentioning

confidence: 97%

“…In lung cancer, mixed models that combine multifactorial features have been shown to provide superior prognostic benefit [12], [13] [14]. Additionally, Spira et al constructed a gene expression biomarker model to predict lung cancer in smokers and then tested it in combination with clinical information, suggesting that an integrated model provided superior specificity for diagnosis [15].…”

Section: Introductionmentioning

confidence: 99%

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A Predictive Model for Personalized Therapeutic Interventions in Non-small Cell Lung Cancer

Kureshi

Abidi

Blouin

2016

IEEE J. Biomed. Health Inform.

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Non-small cell lung cancer (NSCLC) constitutes the most common type of lung cancer and is frequently diagnosed at advanced stages. Clinical studies have shown that molecular targeted therapies increase survival and improve quality of life in patients. Nevertheless, the realization of personalized therapies for NSCLC faces a number of challenges including the integration of clinical and genetic data and a lack of clinical decision support tools to assist physicians with patient selection. To address this problem, we used frequent pattern mining to establish the relationships of patient characteristics and tumor response in advanced NSCLC. Univariate analysis determined that smoking status, histology, epidermal growth factor receptor (EGFR) mutation, and targeted drug were significantly associated with response to targeted therapy. We applied four classifiers to predict treatment outcome from EGFR tyrosine kinase inhibitors. Overall, the highest classification accuracy was 76.56% and the area under the curve was 0.76. The decision tree used a combination of EGFR mutations, histology, and smoking status to predict tumor response and the output was both easily understandable and in keeping with current knowledge. Our findings suggest that support vector machines and decision trees are a promising approach for clinical decision support in the patient selection for targeted therapy in advanced NSCLC.

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

A Predictive Model for Personalized Therapeutic Interventions in Non-small Cell Lung Cancer

Kureshi

Abidi

Blouin

2016

IEEE J. Biomed. Health Inform.

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“…Several studies were devoted to prognostic factors search [4] [7]- [9] [11]. Clinical, morphological, genetic factors have been researched as prognostic factors for patients with NSCLC.…”

Section: Discussionmentioning

confidence: 99%

“…In the past decades a lot of investigations were focused on searching for more accurate markers of lung cancer progression. Researchers indicate that molecular markers may be useful in forecasting of treatment outcome and overall survival rate in patients with NSCLC [4] [6]- [9]. Currently large number of markers are offered for patients with NSCLC but none of them shows its high significance in clinical practice [6].…”

Section: Introductionmentioning

confidence: 99%

Usage of Cox-Regression Model for Forecasting of Survival Rate in Patients with the Early Stage of Non-Small Cell Lung Cancer

Kolesnik¹,

Шевченко²,

Lyakh³

et al. 2014

ALC

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In the past decades a lot of investigations were focused on searching for more accurate markers of lung cancer progression. Researchers indicate that molecular markers may be useful in forecasting of treatment outcome and overall survival rate in patients with non-small cell lung cancer. The aim of our research was to create a forecasting model in order to identify patients with stage I-II of non-small cell lung cancer and dismal prognosis. Our research covered 254 patients with the early stage of non-small cell lung cancer who underwent a cure from June 2008 till December 2012 in the Department of Thoracic Surgery of Zaporizhzhia Regional Clinical Oncologic Dispensary. Surgery was performed for all patients. Adjuvant chemotherapy was performed for 101 patients. In order to carry out multivariate Cox-regression analysis, STATISTICA 6.0 (StatSoft Inc.) program was used. The most significant from 39 variables were selected (tumor size, histological form of tumor, volume of surgical intervention, volume of conducted lymph node dissection, Ki-67 expression, EGFR expression, E-cadherin expression). We propose the computer system which can forecast survival rate in patients with the early stage of non-small cell lung cancer.

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“…In their latest staging manuals, the UICC provides separate tables to describe stage grouping and prognostic grouping, while the AJCC combines both in the same table [11,12]. Sooner or later, as more information is gathered, including molecular and genetic features, this will be the case of lung cancer but, at the moment, the TNM classification of lung cancer remains to be the assessment of its anatomic extent and the strongest predictor of prognosis [40][41][42].…”

Section: The Futurementioning

confidence: 99%

The new TNM classification of lung cancer in practice

Rami–Porta

Giroux

Goldstraw

2011

Breathe

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Educational aimsµ To describe the changes in the 7th edition of the TNM classification of lung cancer µ To discuss the clinical implications of the incorporation of the new TNM classification in everyday practice µ To emphasise the key points for a proper pathologic classification SummaryThe 7th edition of the TNM (tumour, node, metastases) classification of lung cancer incorporates the proposals of the International Association for the Study of Lung Cancer, whose database included more than 100,000 patients from Asia, Australia, Europe, and North America. The changes affect the T and the M components of the classification, and the stage grouping. The N component remained unaltered, although the present descriptors were validated both in the clinical and pathologic settings. This new TNM classification applies to non-small cell lung cancer, small cell lung cancer and, for the first time, to broncho-pulmonary carcinoids. The innovations allow for a better separation of tumours with significantly different prognosis, and imply a more careful determination of tumour size.

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Composite anatomical-clinical-molecular prognostic model in nonsmall cell lung cancer

Cited by 14 publications

References 28 publications

A Predictive Model for Personalized Therapeutic Interventions in Non-small Cell Lung Cancer

A Predictive Model for Personalized Therapeutic Interventions in Non-small Cell Lung Cancer

Usage of Cox-Regression Model for Forecasting of Survival Rate in Patients with the Early Stage of Non-Small Cell Lung Cancer

The new TNM classification of lung cancer in practice

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