Trial reportCPT: pharmacometrics & systems pharmacology2018
Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology.
Trial report in CPT: pharmacometrics & systems pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- Longitudinal analysis of organ-specific tumor lesion sizes in metastatic colorectal cancer patients receiving first line standard chemotherapy in combination with anti-angiogenic treatment.Journal of pharmacokinetics and pharmacodynamics · 2020Trial
- AI-Powered Lesion-Level Tumor Growth Inhibition Modeling Improves Model Stability and Prognostic Association With PFS.CPT: pharmacometrics & systems pharmacology · 2026Article
- Predicting survival in prospective clinical trials using weakly-supervised QSP.NPJ precision oncology · 2025Article
- Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare.Pharmaceutics · 2024Review
- Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities.The AAPS journal · 2021Article
- Tumor growth inhibition modeling of individual lesion dynamics and interorgan variability in HER2-negative breast cancer patients treated with docetaxel.CPT: pharmacometrics & systems pharmacology · 2021Article
- Radiomics Response Signature for Identification of Metastatic Colorectal Cancer Sensitive to Therapies Targeting EGFR Pathway.Journal of the National Cancer Institute · 2020Article
- Identification of Non-Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics.Clinical cancer research : an official journal of the American Association for Cancer Research · 2020Article
- Progress and Opportunities to Advance Clinical Cancer Therapeutics Using Tumor Dynamic Models.Clinical cancer research : an official journal of the American Association for Cancer Research · 2020Review
- Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival.The AAPS journal · 2020Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mathematical models of tumor dynamics generally omit information on individual target lesions (iTLs), and consider the most important variable to be the sum of tumor sizes (TS). However, differences in lesion dynamics might be predictive of tumor progression. To exploit this information, we have developed a novel and flexible approach for the non-parametric analysis of iTLs, which integrates knowledge from signal processing and machine learning. We called this new methodology ClassIfication Clustering of Individual Lesions (CICIL). We used CICIL to assess similarities among the TS dynamics of 3,223 iTLs measured in 1,056 patients with metastatic colorectal cancer treated with cetuximab combined with irinotecan, in two phase II studies. We mainly observed similar dynamics among lesions within the same tumor site classification. In contrast, lesions in anatomic locations with different features showed different dynamics in about 35% of patients. The CICIL methodology has also been implemented in a user-friendly and efficient Java-based framework.
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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.