Prognosis and Treatment of Non–Small Cell Lung Cancer in the Age of Deep Learning

Howard, Frederick M.; Pearson, Alexander T.

doi:10.1001/jamanetworkopen.2020.6368

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…Age is associated with the incidence and prognosis of many cancers, and studies have shown that age is an independent risk factor for cancers such as non-small cell lung cancer and breast cancer 11 , 12 . Previous studies showed that about 90% of bladder cancer patients are over 55 years old, and the average age at diagnosis is 73 years old 13 .…”

Section: Discussionmentioning

confidence: 99%

Surveillance of prognostic risk factors in patients with SCCB using artificial intelligence: a retrospective study

Zhanghuang

Zhang

Wang

et al. 2023

Sci Rep

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Small cell carcinoma of the bladder (SCCB) is a rare urological tumor. The prognosis of SCCB is abysmal. Therefore, this study aimed to construct nomograms that predict overall survival (OS) and cancer-specific survival (CSS) in SCCB patients. Information on patients diagnosed with SCCB during 2004–2018 was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models analyzed Independent risk factors affecting patients' OS and CSS. Nomograms predicting the OS and CSS were constructed based on the multivariate Cox regression model results. The calibration curve verified the accuracy and reliability of the nomograms, the concordance index (C-index), and the area under the curve (AUC). Decision curve analysis (DCA) assessed the potential clinical value. 975 patients were included in the training set (N = 687) and the validation set (N = 288). Multivariate COX regression models showed that age, marital status, AJCC stage, T stage, M stage, surgical approach, chemotherapy, tumor size, and lung metastasis were independent risk factors affecting the patients' OS. However, distant lymph node metastasis instead AJCC stage is the independent risk factor affecting the CSS in the patients. We successfully constructed nomograms that predict the OS and CSS for SCCB patients. The C index of the training set and the validation set of the OS were 0.747 (95% CI 0.725–0.769) and 0.765 (95% CI 0.736–0.794), respectively. The C index of the CSS were 0.749 (95% CI 0.710–0.773) and 0.786 (95% CI 0.755–0.817), respectively, indicating that the predictive models of the nomograms have excellent discriminative power. The calibration curve and the AUC also show good accuracy and discrimination of the nomograms. To sum up, We established nomograms to predict the OS and CSS of SCCB patients. The nomograms have undergone internal cross-validation and show good accuracy and reliability. The DCA shows that the nomograms have an excellent clinical value that can help doctors make clinical-assisted decision-making.

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Section: Discussionmentioning

confidence: 99%

Surveillance of prognostic risk factors in patients with SCCB using artificial intelligence: a retrospective study

Zhanghuang

Zhang

Wang

et al. 2023

Sci Rep

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“…These models can capture non-linear relationships that traditional statistical methods may miss. At present, particular attention is paid to deep learning and its contribution to cancer research; see, for instance [24]. Here, however, we would like to briefly cite some research sectors in which our neural approach to small datasets can benefit cancer studies.…”

Section: Possible Applications In the Medical Fieldmentioning

confidence: 99%

A Neural Modelling Tool for Non-Linear Influence Analyses and Perspectives of Applications in Medical Research

Pasini,

Amendola

2024

Applied Sciences

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Neural network models are often used to analyse non-linear systems; here, in cases of small datasets, we review our complementary approach to deep learning with the purpose of highlighting the importance and roles (linear, non-linear or threshold) of certain variables (assumed as causal) in determining the behaviour of a target variable; this also allows us to make predictions for future scenarios of these causal variables. We present a neural tool endowed with an ensemble strategy and its applications to influence analyses in terms of pruning, attribution and future predictions (free code issued). We describe some case studies on climatic applications which show reliable results and the potentialities of our method for medical studies. The discovery of the importance and role (linear, non-linear or threshold) of causal variables and the possibility of applying the relationships found to future scenarios could lead to very interesting applications in medical research and the study and treatment of cancer, which are proposed in this paper.

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“…Age plays a major role in the onset, progression and prognosis of disease [9] . The immune system of older people undergoes a remodeling process during ageing compared to younger people, which reduces the function and anti-tumor capacity of various immune cells, thereby reducing the e cacy of immune checkpoint inhibitors [10][11] . Therefore, a systematic evaluation and Meta-analysis on the therapeutic effects of immune checkpoint inhibitors on non-small cell lung cancer patients with different ages was conducted in this study, with a view to providing a reference for clinical treatment.…”

Section: Introductionmentioning

confidence: 99%

Immune checkpoint inhibitor for different age patients with NSCLC in efficacy: a systematic review and Meta-analysis

Zhang

Zheng²,

et al. 2022

Preprint

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Objective This article is a Meta-analysis aiming to systematically evaluate the difference in efficacy of immune checkpoint inhibitor in patients with non-small cell lung cancer (NSCLC) by age. Methods We performed a Meta-analysis of published randomized controlled trials concerning for patients with NSCLC by age. We compared overall survival among three groups (age < 65 years, age ≥ 65 years, age ≥ 75 years). Hazard ratios (HRs) and 95% confidence intervals (CIs) were collected and pooled. Results A total of 10,291 patients from 17 RCTs were included. In the group under age 65 years, immune checkpoint inhibitor can significantly prolong the overall survival of patients with NSCLC (HR = 0.73, 95% CI: 0.66∼0.81, P < 0.00001). Meanwhile, it can extend the overall survival of patients with NSCLC (HR = 0.78, 95% CI:0.71∼0.84, P < 0.00001) in the group older than 65 years. However, it has no significant effect on the overall survival of NSCLC patients (HR = 0.88, 95% CI:0.72∼1.08, P > 0.05) in the group older than 75 years. Conclusions According to the Meta-analysis, immune checkpoint inhibitor can prolong the overall survival of patients with NSCLC between the two groups(age < 65 years and age ≥ 65 years). However, it has no significant effect on the overall survival in the group older than 75 years, which may be related to the poor physique of elderly patients and impacted by other diseases.

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Prognosis and Treatment of Non–Small Cell Lung Cancer in the Age of Deep Learning

Cited by 7 publications

References 7 publications

Surveillance of prognostic risk factors in patients with SCCB using artificial intelligence: a retrospective study

Surveillance of prognostic risk factors in patients with SCCB using artificial intelligence: a retrospective study

A Neural Modelling Tool for Non-Linear Influence Analyses and Perspectives of Applications in Medical Research

Immune checkpoint inhibitor for different age patients with NSCLC in efficacy: a systematic review and Meta-analysis

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