Early GLS changes detection after chemoradiation in locally advanced non-small cell lung cancer (NSCLC)

Mega, Simona; Fiore, Michele; Carpenito, Myriam; Novembre, Maria Laura; Miele, M.; Trodella, L.; Grigioni, Francesco; Ippolito, Edy; Ramella, Sara

doi:10.1007/s11547-022-01557-7

Cited by 5 publications

(5 citation statements)

References 36 publications

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“…Treatment options for LM may include local therapies, such as thermal ablation techniques and stereotactic ablative body radiation [ 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 ], chemotherapy [ 97 , 98 ] and, in selected cases, surgery [ 99 , 100 , 101 ]. The introduction of new systemic treatments, including immunotherapy and target therapies, has improved the prognosis of patients with metastatic tumors, but only a subset of mutated patients can benefit from such treatments [ 102 ].…”

Section: Discussionmentioning

confidence: 99%

Radiomics in Lung Metastases: A Systematic Review

Gabelloni

Faggioni

Fusco

et al. 2023

JPM

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Due to the rich vascularization and lymphatic drainage of the pulmonary tissue, lung metastases (LM) are not uncommon in patients with cancer. Radiomics is an active research field aimed at the extraction of quantitative data from diagnostic images, which can serve as useful imaging biomarkers for a more effective, personalized patient care. Our purpose is to illustrate the current applications, strengths and weaknesses of radiomics for lesion characterization, treatment planning and prognostic assessment in patients with LM, based on a systematic review of the literature.

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Section: Discussionmentioning

confidence: 99%

Radiomics in Lung Metastases: A Systematic Review

Gabelloni

Faggioni

Fusco

et al. 2023

JPM

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“…Quantitative image features are considered non-invasive biomarkers that reflect the underlying tumour pathophysiology and heterogeneity [135,136].…”

Section: Nodule Characterizationmentioning

confidence: 99%

Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Cellina,

Cacioppa,

Cè

et al. 2023

Cancers

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Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients’ survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients’ outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called “virtual biopsy”. This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.

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“…More generally, radiomic models outperformed the clinical radiological model in discrimination against both PDACs and other lesions, reaching even higher values when a holistic model incorporated clinical and radiomics features was developed [ 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 ].…”

Section: Tumor Classificationmentioning

confidence: 99%

Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and limits of Radiomics

De Muzio,

Pellegrino,

Fusco

et al. 2023

Diagnostics

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Neuroendocrine neoplasms (NENs) are a group of lesions originating from cells of the diffuse neuroendocrine system. NENs may involve different sites, including the gastrointestinal tract (GEP-NENs). The incidence and prevalence of GEP-NENs has been constantly rising thanks to the increased diagnostic power of imaging and immuno–histochemistry. Despite the plethora of biochemical markers and imaging techniques, the prognosis and therapeutic choice in GEP-NENs still represents a challenge, mainly due to the great heterogeneity in terms of tumor lesions and clinical behavior. The concept that biomedical images contain information about tissue heterogeneity and pathological processes invisible to the human eye is now well established. From this substrate comes the idea of radiomics. Computational analysis has achieved promising results in several oncological settings, and the use of radiomics in different types of GEP-NENs is growing in the field of research, yet with conflicting results. The aim of this narrative review is to provide a comprehensive update on the role of radiomics on GEP-NEN management, focusing on the main clinical aspects analyzed by most existing reports: predicting tumor grade, distinguishing NET from other tumors, and prognosis assessment.

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Early GLS changes detection after chemoradiation in locally advanced non-small cell lung cancer (NSCLC)

Cited by 5 publications

References 36 publications

Radiomics in Lung Metastases: A Systematic Review

Radiomics in Lung Metastases: A Systematic Review

Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and limits of Radiomics

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