Prognostic value of two geriatric screening tools in a cohort of older patients with early stage Non-Small Cell Lung Cancer treated with hypofractionated stereotactic radiotherapy

Cuccia, Francesco; Mortellaro, Gianluca; Mazzola, Rosario; Donofrio, Alessandra; Valenti, Vito; Tripoli, Antonella; Matranga, Domenica; Casto, Antonio Lo; Failla, Giuseppe; Miceli, Giuseppe Di; Ferrera, Giuseppe

doi:10.1016/j.jgo.2019.05.002

Cited by 25 publications

(8 citation statements)

References 41 publications

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“…However, these studies were conducted in study populations that included various types of cancers, and the majority of the patients were diagnosed with metastatic diseases upon inclusion. In general, patients with advanced cancer may be susceptible to having lower G-8 scores than patients with localized cancer; however, Maebayashi et al and Cuccia et al reported abnormal G-8 scores in study populations of older patients with localized NSCLC who were receiving SBRT that were comparable to the scores of patients in our study of 81% and 40%, respectively [28,29]. For both studies, the patients were ≥65 years of age with treatment decisions based on a multidisciplinary assessment and an ECOG-PS of 1 or a Karnofsky performance status of ≥70.…”

Section: Discussionsupporting

confidence: 48%

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Combination of the G-8 Screening Tool and Hand-Grip Strength to Predict Long-Term Overall Survival in Non-Small Cell Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy

Bentsen

Hansen

Ryg

et al. 2021

Cancers

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The Geriatric 8 (G-8) is a known predictor of overall survival (OS) in older cancer patients, but is mainly based on nutritional aspects. This study aimed to assess if the G-8 combined with a hand-grip strength test (HGST) in patients with NSCLC treated with stereotactic body radiotherapy can predict long-term OS better than the G-8 alone. A total of 46 SBRT-treated patients with NSCLC of stage T1-T2N0M0 were included. Patients were divided into three groups: fit (normal G-8 and HGST), vulnerable (abnormal G-8 or HGST), or frail (abnormal G-8 and HGST). Statistically significant differences were found in 4-year OS between the fit, vulnerable, and frail groups (70% vs. 46% vs. 25%, p = 0.04), as well as between the normal and abnormal G-8 groups (69% vs. 39%, p = 0.02). In a multivariable analysis of OS, being vulnerable with a hazard ratio (HR) of 2.03 or frail with an HR of 3.80 indicated poorer OS, but this did not reach statistical significance. This study suggests that there might be a benefit of adding a physical test to the G-8 for more precisely predicting overall survival in SBRT-treated patients with localized NSCLC. However, this should be confirmed in a larger study population.

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Section: Discussionsupporting

confidence: 48%

“…Nevertheless, a subanalysis revealed that 73% of patients aged 70 or older in our study had abnormal G-8 scores, showing a similar distribution to that seen across the entire study population. Thus, this study population represented a group of patients that was comparable to the populations of both Maebayashi et al and Cuccia et al [28,29].…”

Section: Discussionmentioning

confidence: 93%

Combination of the G-8 Screening Tool and Hand-Grip Strength to Predict Long-Term Overall Survival in Non-Small Cell Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy

Bentsen

Hansen

Ryg

et al. 2021

Cancers

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“…Because of their small numbers and underpowered nature, these studies have produced conflicting results and have left outstanding knowledge gaps. [66][67][68] For instance, although geriatric assessment tools (e.g., Vulnerable Elders Survey-13 69 and G8 screening tools 70,71 ) have predicted RT incompletion in patients older than age 75, 69 mortality in patients older than age 70 with head and neck cancer receiving chemoradiation, 72 and late-occurring 68 (but not acute 73 ) adverse effects from lung irradiation, post-RT functional status was not examined in any of these studies. 60 In another analysis of 46 patients undergoing chemoradiation, pre-RT functional disability, defined as a score of 14 or lower on the Instrumental Activities of Daily Living scale, predicted lower quality of life and increased patient-reported toxicities 6 weeks after RT, but did not predict treatment delays, change in prescribed therapy, hospitalization, or death.…”

Section: Disparities In the Care Of Older Adults: A Radiation Oncologist's Perspectivementioning

confidence: 99%

Care Disparities Across the Health Care Continuum for Older Adults: Lessons From Multidisciplinary Perspectives

Dharmarajan

Presley

Wyld

2021

American Society of Clinical Oncology Educational Book

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Older adults comprise a considerable proportion of patients with cancer in the world. Across multiple cancer types, cancer treatment outcomes among older age groups are often inferior to those among younger adults. Cancer care for older individuals is complicated by the need to adapt treatment to baseline health, fitness, and frailty, all of which vary widely within this age group. Rates of social deprivation and socioeconomic disparities are also higher in older adults, with many living on reduced incomes, further compounding health inequality. It is important to recognize and avoid undertreatment and overtreatment of cancer in this age group; however, simply addressing this problem by mandating standard treatment of all would lead to harms resulting from treatment toxicity and futility. However, there is little high-quality evidence on which to base these decisions, because older adults are poorly represented in clinical trials. Clinicians must recognize that simple extrapolation of outcomes from younger age cohorts may not be appropriate because of variance in disease stage and biology, variation in fitness and treatment tolerance, and reduced life expectancy. Older patients may also have different life goals and priorities, with a greater focus on quality of life and less on length of life at any cost. Health care professionals struggle with treatment of older adults with cancer, with high rates of variability in practice between and within countries. This suggests that better national and international recommendations that more fully address the needs of this special patient population are required and that primary research focused on the older age group is urgently required to inform these guidelines.

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“…However, the inability to obtain complete information on heterogeneous tumors remains a limitation of these invasive methods [20,21]. Similarly, patients with clinically suspected NSCLC, especially elderly ones [22], may have medical comorbidities that increase biopsy risks, making them more likely to receive stereotactic body radiation therapy (SBRT) without a biopsy [21].…”

Section: Introductionmentioning

confidence: 99%

Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples

Ubaldi

Valenti²,

Borgese

et al. 2021

Physica Medica

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Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC).We analyzed the radiotherapy planning thoracic CT scans of a proprietary sample of 47 subjects (L-RT) and integrated this dataset with a publicly available set of 130 patients from the MAASTRO NSCLC collection (Lung1). We implemented intra-and inter-sample cross-validation strategies (CV) for evaluating the ML predictive model performances with not so large datasets.We carried out two classification tasks: histology classification (3 classes) and overall stage classification (two classes: stage I and II). In the first task, the best performance was obtained by a Random Forest classifier, once the analysis has been restricted to stage I and II tumors of the Lung1 and L-RT merged dataset (AUC = 0.72 ± 0.11). For the overall stage classification, the best results were obtained when training on Lung1 and testing of L-RT dataset (AUC = 0.72 ± 0.04 for Random Forest and AUC = 0.84 ± 0.03 for linear-kernel Support Vector Machine).According to the classification task to be accomplished and to the heterogeneity of the available dataset(s), different CV strategies have to be explored and compared to make a robust assessment of the potential of a predictive model based on radiomics and ML.

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Prognostic value of two geriatric screening tools in a cohort of older patients with early stage Non-Small Cell Lung Cancer treated with hypofractionated stereotactic radiotherapy

Cited by 25 publications

References 41 publications

Combination of the G-8 Screening Tool and Hand-Grip Strength to Predict Long-Term Overall Survival in Non-Small Cell Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy

Combination of the G-8 Screening Tool and Hand-Grip Strength to Predict Long-Term Overall Survival in Non-Small Cell Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy

Care Disparities Across the Health Care Continuum for Older Adults: Lessons From Multidisciplinary Perspectives

Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples

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