ReviewAmerican Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting2018
Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications.
Review in American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
47 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Clinical application of radiomics for the prediction of treatment outcome and survival in patients with renal cell carcinoma: a systematic review.World journal of urology · 2024Pooled it
- Delta radiomics: an updated systematic review.La Radiologia medica · 2024Pooled it
- Novel cancer therapies for advanced cutaneous melanoma: The added value of radiomics in the decision making process-A systematic review.Cancer medicine · 2020Pooled it
- Neoadjuvant cabozantinib for locally advanced nonmetastatic clear cell renal cell carcinoma: a phase 2 trial.Nature cancer · 2025Trial
- The prognostic value of radiomic features from pre- and post-treatmentScientific reports · 2023Trial
- CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy.European radiology · 2022Trial
- An interpretable machine learning model for predicting visceral pleural invasion in cT1 lung adenocarcinoma based on habitat analysis.Quantitative imaging in medicine and surgery · 2025Article
- Radiation enteritis associated with temporal sequencing of total neoadjuvant therapy in locally advanced rectal cancer: a preliminary study.Radiation oncology (London, England) · 2025Article
- Attention mechanism-based habitat analysis for predicting pleural invasion and prognosis of pulmonary nodules.Translational lung cancer research · 2025Article
- Multiparametric MRI and artificial intelligence in predicting and monitoring treatment response in bladder cancer.Insights into imaging · 2025Article
- Predicting the immune therapy response of advanced non-small cell lung cancer based on primary tumor and lymph node radiomics features.Frontiers in medicine · 2025Article
- Article
- Differential Radiomics-Based Signature Predicts Lung Cancer Risk Accounting for Imaging Parameters in NLST Cohort.Cancer medicine · 2024Article
- Review
- Transfer learning approach in pre-treatment CT images to predict therapeutic response in advanced malignant pleural mesothelioma.Frontiers in oncology · 2024Article
- A Novel Method for Evaluating Early Tumor Response Based on Daily CBCT Images for Lung SBRT.Cancers · 2023Article
- Digital pathology systems enabling quality patient care.Genes, chromosomes & cancer · 2023Review
- Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas.Oral oncology · 2023Article
- Noninvasive imaging-based machine learning algorithm to identify progressive disease in advanced hepatocellular carcinoma receiving second-line systemic therapy.Scientific reports · 2023Article
- Combination of clinical, radiomic, and "delta" radiomic features in survival prediction of metastatic gastroesophageal adenocarcinoma.Frontiers in oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The current standard of Response Evaluation Criteria in Solid Tumors (RECIST)-based tumor response evaluation is limited in its ability to accurately monitor treatment response. Radiomics, an approach involving computerized extraction of several quantitative imaging features, has shown promise in predicting as well as monitoring response to therapy. In this article, we provide a brief overview of radiomic approaches and the various analytical methods and techniques, specifically in the context of predicting and monitoring treatment response for non-small cell lung cancer (NSCLC). We briefly summarize some of the various types of radiomic features, including tumor shape and textural patterns, both within the tumor and within the adjacent tumor microenvironment. Additionally, we also discuss work in delta-radiomics or change in radiomic features (e.g., texture within the nodule) across longitudinally interspersed images in time for monitoring changes in therapy. We discuss the utility of these approaches for NSCLC, specifically the role of radiomics as a prognostic marker for treatment effectiveness and early therapy response, including chemoradiation, immunotherapy, and trimodality therapy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.