Journal of cancer research and clinical oncology
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J. Cancer Res. Clin. Oncol. · Jan 2020
Microwave ablation as local consolidative therapy for patients with extracranial oligometastatic EGFR-mutant non-small cell lung cancer without progression after first-line EGFR-TKIs treatment.
Evidence from multiple clinical trials showed that local consolidative therapy (LCT) improved survival in oligometastatic non-small cell lung cancer (NSCLC) patients. In the present study, we aim to explore the potential role of microwave ablation (MWA) as LCT for epidermal growth factor receptor (EGFR)-mutant advanced NSCLC patients with extracranial oligometastasis. ⋯ Patients with MWA consolidation therapy had significantly improved PFS (median 16.7 vs. 12.9 months, HR 0.44, 95% CI 0.22-0.88, P = 0.02) and OS (median: 34.8 vs. 22.7 months, HR 0.45, 95% CI 0.24-0.88, P = 0.04) than monotherapy group. MWA for LCT was identified as the independent predictive factor for better PFS (HR 0.46, 95% CI 0.37-0.82, P < 0.01) and OS (HR 0.57, 95% CI 0.33-0.91, P = 0.02). Most toxicities were mild and well tolerated. No patient had to discontinue EGFR-TKIs because of MWA complications. These findings suggest that MWA as local consolidative therapy after first-line EGFR-TKIs treatment leads to better disease control and survival than TKIs monotherapy in EGFR-mutant advanced NSCLC patients with extracranial oligometastasis. MWA as a novel option of LCT might be considered for clinical management of these patients.
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J. Cancer Res. Clin. Oncol. · Jan 2020
ReviewSeparating or combining immune checkpoint inhibitors (ICIs) and radiotherapy in the treatment of NSCLC brain metastases.
With the advancement of imaging technology, systemic disease control rate and survival rate, the morbidity of brain metastases (BMs) from non-small cell lung cancer (NSCLC) has been riding on a steady upward trend (40%), but management of BMs from NSCLC remains obscure. Systemic therapy is anticipated to offer novel therapeutic avenues in the management of NSCLC BMs, and radiotherapy (RT) and immunotherapy have their own advantages. Recently, it was confirmed that immune checkpoint inhibitors (ICIs) and RT could mutually promote the efficacy in the treatment of BMs from NSCLC. In this paper, we provide a review on current understandings and practices of separating or combining ICIs and RT, which could provide a reference for the coming laboratory and clinical studies and contribute to the development of new approaches in NSCLC BMs.
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J. Cancer Res. Clin. Oncol. · Jan 2020
ReviewEvolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades' development course and future prospect.
Lung cancer is the commonest cause of cancer deaths worldwide, and its mortality can be reduced significantly by performing early diagnosis and screening. Since the 1960s, driven by the pressing needs to accurately and effectively interpret the massive volume of chest images generated daily, computer-assisted diagnosis of pulmonary nodule has opened up new opportunities to relax the limitation from physicians' subjectivity, experiences and fatigue. And the fair access to the reliable and affordable computer-assisted diagnosis will fight the inequalities in incidence and mortality between populations. It has been witnessed that significant and remarkable advances have been achieved since the 1980s, and consistent endeavors have been exerted to deal with the grand challenges on how to accurately detect the pulmonary nodules with high sensitivity at low false-positive rate as well as on how to precisely differentiate between benign and malignant nodules. There is a lack of comprehensive examination of the techniques' development which is evolving the pulmonary nodules diagnosis from classical approaches to machine learning-assisted decision support. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the computer-assisted nodules detection and benign-malignant classification techniques developed over three decades, which have evolved from the complicated ad hoc analysis pipeline of conventional approaches to the simplified seamlessly integrated deep learning techniques. This review also identifies challenges and highlights opportunities for future work in learning models, learning algorithms and enhancement schemes for bridging current state to future prospect and satisfying future demand. ⋯ It is the first literature review of the past 30 years' development in computer-assisted diagnosis of lung nodules. The challenges indentified and the research opportunities highlighted in this survey are significant for bridging current state to future prospect and satisfying future demand. The values of multifaceted driving forces and multidisciplinary researches are acknowledged that will make the computer-assisted diagnosis of pulmonary nodules enter into the main stream of clinical medicine and raise the state-of-the-art clinical applications as well as increase both welfares of physicians and patients. We firmly hold the vision that fair access to the reliable, faithful, and affordable computer-assisted diagnosis for early cancer diagnosis would fight the inequalities in incidence and mortality between populations, and save more lives.