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dc.creatorVera-Yunca, D. (Diego)-
dc.creatorGirard, P. (Pascal.)-
dc.creatorParra-Guillen, Z.P. (Zinnia Patricia)-
dc.creatorMunafo, A. (Alain)-
dc.creatorTroconiz, I.F. (Iñaki F.)-
dc.creatorTerranova, N. (Nadia)-
dc.identifier.citationVera-Yunca, D. (Diego); Girard, P. (Pascal.); Parra-Guillen, Z.P. (Zinnia Patricia); et al. "Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival". The AAPS Journal. 22 (58), 2020,es
dc.description.abstractTotal tumor size (TS) metrics used in TS models in oncology do not consider tumor heterogeneity, which could help to better predict drug efficacy. We analyzed individual target lesions (iTLs) of patients with metastatic colorectal carcinoma (mCRC) to determine differences in TS dynamics by using the ClassIfication Clustering of Individual Lesions (CICIL) methodology. Results from subgroup analyses comparing genetic mutations and TS metrics were assessed and applied to survival analyses. Data from four mCRC clinical studies were analyzed (1781 patients, 6369 iTLs). CICIL was used to assess differences in lesion TS dynamics within a tissue (intra-class) or across different tissues (inter-class). First, lesions were automatically classified based on their location. Cross-correlation coefficients (CCs) determined if each pair of lesions followed similar or opposite dynamics. Finally, CCs were grouped by using the K-means clustering method. Heterogeneity in tumor dynamics was lower in the intra-class analysis than in the inter-class analysis for patients receiving cetuximab. More tumor heterogeneity was found in KRAS mutated patients compared to KRAS wild-type (KRASwt) patients and when using sum of longest diameters versus sum of products of diameters. Tumor heterogeneity quantified as the median patient’s CC was found to be a predictor of overall survival (OS) (HR = 1.44, 95% CI 1.08–1.92), especially in KRASwt patients. Intra- and inter-tumor tissue heterogeneities were assessed with CICIL. Derived metrics of heterogeneity were found to be a predictor of OS time. Considering differences between lesions’ TS dynamics could improve oncology models in favor of a better prediction of OS.es_ES
dc.subjectIndividual tumor lesion dynamicses_ES
dc.subjectMetastatic colorectal canceres_ES
dc.subjectSurvival analysises_ES
dc.subjectTumor size modelinges_ES
dc.titleMachine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survivales_ES
dc.description.noteThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.es_ES
dc.editorial.noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.es_ES
dadun.citation.publicationNameThe AAPS Journales_ES

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