Icotinib and whole-brain radiotherapy for the treatment in patients with brain metastases from EGFR-mutant nonsmall cell lung cancer

Jiang, Aiying; Zhang, Jing; Luo, Hailong; Gao, Feng; Lv, Yufeng

doi:10.1097/md.0000000000010312

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Thus, an individualized method is needed to develop precision therapy strategy and to predict prognosis for EGFR-mutated NSCLC patients [14].…”

Section: Introductionmentioning

confidence: 99%

A Computer Tomography Radiomics-based model to Predict Survival of Patients with EGFR‑Mutated Non‑small‑cell Lung Cancer

Huang

Cao

et al. 2022

Preprint

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Background To evaluate the prognosis of EGFR-mutated non-small-cell lung cancer(NSCLC) patients and accurately target the patient subgroups with best potential outcome greatest, a simpler and more effective model based on readily available laboratory parameters is needed in clinical practice. Methods Totally, computed tomographic (CT) images from 162 EGFR-mutated NSCLC patients at the time of first diagnosis in West China Hospital of Sichuan University were retrospectively collected and analysed to describe the target area of the lesion. All patients were randomly divided into a training cohort and a validation cohort. Radiomic features from images were extracted. The least absolute shrinkage selection operator (LASSO) regression method was used to screen valuable radiomic features. The logistic regression method was used to establish a radiomic model, and the nomogram was used to evaluate the discrimination ability. Receiver characteristic curve (ROC) and decision curve analysis (DCA) were used to evaluate the performance of the model. Results We established a nomogram that combined 6 important clinical factors and imaging risk scores to predict the three-year and five-year survival rates of EGFR-mutated NSCLC patients. The AUC-ROC of three-year OS was 0.715, and the five-year OS was 0.705, which indicated a favourable discrimination capability of our nomogram and its great potential in targeting clinical services and predicting patient outcomes. Conclusion Based on chest CT imaging and clinicopathological features, including age, sex, Stage_T, Stage_N, Stage_M, clinical stage, and imaging risk score, we constructed and validated a nomogram for predicting an individualized prediction of survival for patients with EGFR‑mutated NSCLC.

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“…Thus, an individualized method is needed to develop precision therapy strategy and to predict prognosis for EGFR-mutated NSCLC patients [14].…”

Section: Introductionmentioning

confidence: 99%

A Computer Tomography Radiomics-based model to Predict Survival of Patients with EGFR‑Mutated Non‑small‑cell Lung Cancer

Huang

Cao

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Durable response to pulsatile icotinib for central nervous system metastases from EGFR-mutated non-small cell lung cancer: A case report

Xie

et al. 2020

WJCC

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Icotinib and whole-brain radiotherapy for the treatment in patients with brain metastases from EGFR-mutant nonsmall cell lung cancer

Cited by 2 publications

References 23 publications

A Computer Tomography Radiomics-based model to Predict Survival of Patients with EGFR‑Mutated Non‑small‑cell Lung Cancer

A Computer Tomography Radiomics-based model to Predict Survival of Patients with EGFR‑Mutated Non‑small‑cell Lung Cancer

Durable response to pulsatile icotinib for central nervous system metastases from EGFR-mutated non-small cell lung cancer: A case report

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