Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

Terranova, Nadia; Girard, Pascal; Ιωάννου, Κωνσταντίνος; Klinkhardt, Ute; Munafo, Alain

doi:10.1002/psp4.12284

Cited by 14 publications

(25 citation statements)

References 23 publications

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“…The previously developed CICIL methodology, implemented in a Java-based platform, was used to evaluate the similarities or differences between iTL dynamics (interorgans or intra-organ) (11). This methodology consists of 3 steps: (i) iTLs are classified based on their location and type described by expert physicians, (ii) cross-correlation (CC) values are estimated to assess the degree of similarity between dynamics of two lesions, and (iii) similar crosscorrelation values are automatically grouped into clusters using the K-means clustering method (24).…”

Section: Cicil Methodologymentioning

confidence: 99%

“…Step 1: Rule-Based Classification iTLs were classified according to the same methods described in (11), which were defined for two other mCRC clinical studies. Several keywords were defined for each class in the classification text file of CICIL.…”

Section: Cicil Methodologymentioning

confidence: 99%

“…The number of assessments per lesion allowed us to check up to 12 time shifts in both directions. Details on CC calculations are reported in (11).…”

Section: Cicil Methodologymentioning

confidence: 99%

“…The CICIL methodology was performed in its Java-based software version 1.0.4. (11). For the survival analysis, R was used to run statistical tests and models, along with the survival package (29).…”

Section: Softwarementioning

confidence: 99%

“…In a previous work (11), the analysis of iTLs was performed to assess differences in lesions dynamics by using the Classification Clustering of Individual Lesions (CICIL) methodology, which integrates knowledge from signal processing and machine learning (ML). Results were used to then inform decisions about building a longitudinal model for iTLs or for total TS.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival

Vera-Yunca

Girard

Parra-Guillén

et al. 2020

AAPS J

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Total 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.

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Section: Cicil Methodologymentioning

confidence: 99%

Section: Cicil Methodologymentioning

confidence: 99%

“…The number of assessments per lesion allowed us to check up to 12 time shifts in both directions. Details on CC calculations are reported in (11).…”

Section: Cicil Methodologymentioning

confidence: 99%

Section: Softwarementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival

Vera-Yunca

Girard

Parra-Guillén

et al. 2020

AAPS J

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Current Status and Future Directions: The Application of Artificial Intelligence/Machine Learning for Precision Medicine

Naik,

Goyal,

Foschini

et al. 2024

Clin Pharma and Therapeutics

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Technological innovations, such as artificial intelligence (AI) and machine learning (ML), have the potential to expedite the goal of precision medicine, especially when combined with increased capacity for voluminous data from multiple sources and expanded therapeutic modalities; however, they also present several challenges. In this communication, we first discuss the goals of precision medicine, and contextualize the use of AI in precision medicine by showcasing innovative applications (e.g., prediction of tumor growth and overall survival, biomarker identification using biomedical images, and identification of patient population for clinical practice) which were presented during the February 2023 virtual public workshop entitled “Application of Artificial Intelligence and Machine Learning for Precision Medicine,” hosted by the US Food and Drug Administration (FDA) and University of Maryland Center of Excellence in Regulatory Science and Innovation (M‐CERSI). Next, we put forward challenges brought about by the multidisciplinary nature of AI, particularly highlighting the need for AI to be trustworthy. To address such challenges, we subsequently note practical approaches, viz., differential privacy, synthetic data generation, and federated learning. The proposed strategies – some of which are highlighted presentations from the workshop – are for the protection of personal information and intellectual property. In addition, methods such as the risk‐based management approach and the need for an agile regulatory ecosystem are discussed. Finally, we lay out a call for action that includes sharing of data and algorithms, development of regulatory guidance documents, and pooling of expertise from a broad‐spectrum of stakeholders to enhance the application of AI in precision medicine.

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Machine Learning in Modeling Disease Trajectory and Treatment Outcomes: An Emerging Enabler for Model‐Informed Precision Medicine

Terranova,

Venkatakrishnan

2024

Clin Pharma and Therapeutics

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The increasing breadth and depth of resolution in biological and clinical data, including ‐omics and real‐world data, requires advanced analytical techniques like artificial intelligence (AI) and machine learning (ML) to fully appreciate the impact of multi‐dimensional population variability in intrinsic and extrinsic factors on disease progression and treatment outcomes. Integration of advanced data analytics in Quantitative Pharmacology is crucial for drug‐disease knowledge management, enabling precise, efficient and inclusive drug development and utilization – an application we refer to as Model‐Informed Precision Medicine. AI/ML enables characterization of the molecular and clinical sources of heterogeneity in disease trajectory, advancing endpoint qualification and biomarker discovery, and informing patient enrichment for Proof‐of‐Concept studies as well as trial designs for efficient evidence generation incorporating digital twins and virtual control arms. Explainable ML methods are valuable in elucidating predictors of efficacy and safety of pharmacological treatments, thereby informing response monitoring and risk mitigation strategies. In oncology, emerging opportunities exist for development of the next generation of disease models via ML‐assisted joint longitudinal modeling of high‐dimensional biomarker data such as circulating tumor DNA and radiomics profiles as predictors of survival outcomes. Finally, mining real world data leveraging ML algorithms enables understanding of the impact of exclusion criteria on clinical outcomes, thereby informing rational design of appropriately inclusive clinical trials through data‐driven broadening of eligibility criteria. Herein, we provide an overview of the aforementioned contexts of use of ML in drug‐disease modeling based on examples across multiple therapeutic areas including neurology, rare diseases, autoimmune diseases, oncology and immuno‐oncology.

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Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

Cited by 14 publications

References 23 publications

Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival

Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival

Current Status and Future Directions: The Application of Artificial Intelligence/Machine Learning for Precision Medicine

Machine Learning in Modeling Disease Trajectory and Treatment Outcomes: An Emerging Enabler for Model‐Informed Precision Medicine

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