Predicting 5-Year Progression and Survival Outcomes for Early Stage Non-small Cell Lung Cancer Treated with Stereotactic Ablative Radiation Therapy: Development and Validation of Robust Prognostic Nomograms

Kang, Jingjing; Ning, Matthew S.; Feng, Han; Li, Hongqi; Bahig, Houda; Brooks, Eric D.; Welsh, James W.; Ye, Rui; Miao, Hongyu; Chang, Joe Y.

doi:10.1016/j.ijrobp.2019.09.037

Cited by 30 publications

(26 citation statements)

References 43 publications

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“…CCI, a comprehensive index of multi-morbidities, is a good indicator of a patient’s global status and has been demonstrated to affect the prognosis of patients with lung cancer [ 17 , 23 ]. The variables in the CCI model are readily available and scores can easily be calculated by physicians.…”

Section: Discussionmentioning

confidence: 99%

“…The variables in the CCI model are readily available and scores can easily be calculated by physicians. Recent studies have reached controversial conclusions on the prognostic significance of the CCI [ 17 , 23 , 24 ]. Additionally, no studies have investigated the predictive significance of CCI in patients with NSCLC receiving treatment with PD1 inhibitors; accordingly, so we used a cutoff point of 1 for further investigation, as used by Pylvalainen et al [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

“…The simplified comorbidity score (SCS) and Charlson comorbidity index (CCI) are the 2 most extensively validated scoring systems for assessing comorbidities and predicting prognosis [ 15 , 16 ]. These 2 comorbidity indices have previously been used as prognostic factors in patients with various carcinomas [ 17 – 19 ]. Of interest, the SCS was designed specifically for lung cancer [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

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Effect of Comorbidity on Outcomes of Patients with Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD1 Immunotherapy

Zeng

Zhu

Xu³

et al. 2020

Med Sci Monit

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of Comorbidity on Outcomes of Patients with Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD1 Immunotherapy

Zeng

Zhu

Xu³

et al. 2020

Med Sci Monit

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“…The c-index varies between 0 and 1, where a c-index of 1 means a perfect prediction, and a c-index of 0.5 can be interpreted as a random assignment. In biomedical applications and in particular lung cancer survival analysis, a c-index close to 0.7 is considered as satisfying and acceptable 59 , 60 , however, interpretation of the computed c-index value depends on the dataset and the problem at hand.…”

Section: Methodsmentioning

confidence: 99%

$$\text {DRTOP}$$: deep learning-based radiomics for the time-to-event outcome prediction in lung cancer

Afshar

Mohammadi

Tyrrell

et al. 2020

Sci Rep

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Hand-crafted radiomics has been used for developing models in order to predict time-to-event clinical outcomes in patients with lung cancer. Hand-crafted features, however, are pre-defined and extracted without taking the desired target into account. furthermore, accurate segmentation of the tumor is required for development of a reliable predictive model, which may be objective and a time-consuming task. to address these drawbacks, we propose a deep learning-based radiomics model for the time-to-event outcome prediction, referred to as DRtop that takes raw images as inputs, and calculates the image-based risk of death or recurrence, for each patient. our experiments on an in-house dataset of 132 lung cancer patients show that the obtained image-based risks are significant predictors of the time-to-event outcomes. Computed Tomography (CT)-based features are predictors of the overall survival (OS), with the hazard ratio (HR) of 1.35, distant control (DC), with HR of 1.06, and local control (LC), with HR of 2.66. The Positron Emission Tomography (PET)-based features are predictors of OS and recurrence free survival (RFS), with hazard ratios of 1.67 and 1.18, respectively. the concordance indices of 68% , 63% , and 64% for predicting the oS, Dc, and RfS show that the deep learning-based radiomics model is as accurate or better in predicting predefined clinical outcomes compared to hand-crafted radiomics, with concordance indices of 51% , 64% , and 47% , for predicting the OS, DC, and RFS, respectively. Deep learning-based radiomics has the potential to offer complimentary predictive information in the personalized management of lung cancer patients. Despite significant advancements in treatment, lung cancer remains the leading cause of cancer-related mortalities worldwide 1. Lung cancer is among the most common cancers and, together with breast cancer, includes most of the newly diagnosed cancer cases 2. Significant recent progress in the biological understanding and tumor heterogeneity of non-small cell lung cancer calls for treatment individualization. Specific clinical endpoints are used in clinical trials to measure the clinical benefit of a specific treatment 3,4. Although overall survival (OS) remains the gold standard, other clinical endpoints such as recurrence free survival (RFS), distant control (DC), and local control (LC) measure different and significant aspects of the clinical benefit of treatment. Inherent difficulties to assess these clinical outcomes such as the lengthy duration of the follow-up needed until the time of event and the various parameters, unrelated to the primary cancer, affecting the result during follow-up, have led to a surge for developing surrogates that can predict clinical outcomes noninvasively. Recently, radiomics, which is the process of extracting high throughput quantitative and semi-quantitative features from medical images aiming at diagnosis, classification or prediction of outcomes, has attracted much attention, showing promising results 5-15. Studies, investigating th...

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“…2 There are serious efforts to improve and develop RT efficacy. 3 It is reported that the inhibition of certain signaling pathways is associated with the enhancement of RT response in several cancer types. 4,5 Proteomics is a large-scale method for determining the changed expression proteins after intervention in the samples.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the SRC Inhibition Role in the Efficacy of Breast Cancer Radiotherapy

2019

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Introduction: Radiotherapy (RT) is an effective therapeutic method for preventing the recurrence of breast cancer after surgery. The development and improvement of RT are of interest to scientists. Possible improvement of RT via study of dysregulated proteins of breast cancer cell line MDA-MB-231 which is exposed to 10 Gray (Gy) radiation is aim of this study. Methods: Using protein-protein interaction (PPI) network analysis by means of running Cytoscape software via the STRING database, the up-regulated proteins of MDA-MB-231 breast cancer cells irradiated by a single and fractioned 10 Gy 137Cs γ-radiation were analyzed. The network was analyzed by using the Network analyzer to characterize the central genes. The action map was mapped for the queried genes and the added neighbors via CluePedia-STRING ACTIONS-v10.5- 20.11.2017. Results: The 14 differentially expressed proteins (DEPs) plus 10 neighbors interacted to construct a network. Among the 14 queried DEPs, FN1, CSPG4, LRP1, GSN, RTN4, and CTSD were highlighted as a complex set in the analysis. The analysis revealed that SRC as an added neighbor was activated by the critical DEPs. The activation of other oncogenes like AKT1 was also determined. Conclusion: The results indicate that the inhibition of SRC activity or the inhibition of its activators is a useful function of breast cancer RT

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Predicting 5-Year Progression and Survival Outcomes for Early Stage Non-small Cell Lung Cancer Treated with Stereotactic Ablative Radiation Therapy: Development and Validation of Robust Prognostic Nomograms

Cited by 30 publications

References 43 publications

Effect of Comorbidity on Outcomes of Patients with Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD1 Immunotherapy

Effect of Comorbidity on Outcomes of Patients with Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD1 Immunotherapy

$$\text {DRTOP}$$: deep learning-based radiomics for the time-to-event outcome prediction in lung cancer

Assessment of the SRC Inhibition Role in the Efficacy of Breast Cancer Radiotherapy

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