Predicting cancer outcomes with radiomics and artificial intelligence in radiology
The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the
application of AI-based cancer imaging analysis to address other, more complex, clinical …
application of AI-based cancer imaging analysis to address other, more complex, clinical …
Revisiting neoadjuvant therapy in non-small-cell lung cancer
Despite the rapidly evolving treatment landscape in advanced non-small-cell lung cancer
(NSCLC), developments in neoadjuvant and adjuvant treatments have been nascent by …
(NSCLC), developments in neoadjuvant and adjuvant treatments have been nascent by …
[HTML][HTML] Radiomics and artificial intelligence in lung cancer screening
F Binczyk, W Prazuch, P Bozek… - Translational lung cancer …, 2021 - ncbi.nlm.nih.gov
Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76
million associated deaths reported in 2018. The key issue in the fight against this disease is …
million associated deaths reported in 2018. The key issue in the fight against this disease is …
Harnessing non-destructive 3D pathology
High-throughput methods for slide-free three-dimensional (3D) pathological analyses of
whole biopsies and surgical specimens offer the promise of modernizing traditional …
whole biopsies and surgical specimens offer the promise of modernizing traditional …
[HTML][HTML] Introduction to radiomics for a clinical audience
C McCague, S Ramlee, M Reinius, I Selby, D Hulse… - Clinical Radiology, 2023 - Elsevier
Radiomics is a rapidly develo** field of research focused on the extraction of quantitative
features from medical images, thus converting these digital images into minable, high …
features from medical images, thus converting these digital images into minable, high …
Radiological tumour classification across imaging modality and histology
J Wu, C Li, M Gensheimer, S Padda, F Kato… - Nature machine …, 2021 - nature.com
Radiomics refers to the high-throughput extraction of quantitative features from radiological
scans and is widely used to search for imaging biomarkers for the prediction of clinical …
scans and is widely used to search for imaging biomarkers for the prediction of clinical …
Deep learning in radiology for lung cancer diagnostics: A systematic review of classification, segmentation, and predictive modeling techniques
This study presents a comprehensive systematic review focusing on the applications of deep
learning techniques in lung cancer radiomics. Through a rigorous screening process of 589 …
learning techniques in lung cancer radiomics. Through a rigorous screening process of 589 …
Immunotherapy in non-small cell lung cancer: Past, present, and future directions
SR Punekar, E Shum, CM Grello, SC Lau… - Frontiers in …, 2022 - frontiersin.org
Many decades in the making, immunotherapy has demonstrated its ability to produce
durable responses in several cancer types. In the last decade, immunotherapy has shown …
durable responses in several cancer types. In the last decade, immunotherapy has shown …
Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis
W Kang, X Qiu, Y Luo, J Luo, Y Liu, J **, X Li… - Journal of Translational …, 2023 - Springer
The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has
given rise to the prominence of the tumor microenvironment (TME) as a critical area of …
given rise to the prominence of the tumor microenvironment (TME) as a critical area of …
On the performance of lung nodule detection, segmentation and classification
Computed tomography (CT) screening is an effective way for early detection of lung cancer
in order to improve the survival rate of such a deadly disease. For more than two decades …
in order to improve the survival rate of such a deadly disease. For more than two decades …