Ignite Creation Date:
2024-10-26 @ 3:40 PM
Last Modification Date:
2024-10-26 @ 3:40 PM
Study NCT ID:
NCT06608420
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-09-19
Brief Title:
Precision Medicine for LGCMN and Melanoma 1
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Precision Medicine for LGCMN and Melanoma 1 Precis-mel 1
Status:
RECRUITING
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
Precis-mel 1
Brief Summary:
The primary objective of this study is to create a highly multidimensional and multicentric database for melanoma that encompasses cohorts of children adolescent and young adults This database will be used to perform survival analysis and evaluate sentinel lymph node SLNB positivity in CAYA The secondary objectives to be met are the following
Adaptation and optimization of algorithms to adapt and optimize existing precision medicine algorithms which are currently being utilized in adult patient care for their application within pediatric and young adult populations
Implementation of transfer learning given the limitations associated with pediatric and young adult data we intend to utilize transfer learning techniques The study will employ a sequential waterfall methodology whereby machine learning models trained on adult patient data will be fine-tuned using the more limited data from younger cohorts
Integration of expert medical opinion to integrate physicians39 scientific domain knowledge into the decision support system This will be facilitated through the comprehensive examination of existing literature as well as the evaluation of variable risk contributions within each patient group
AI-based prognostic models to develop artificial intelligence-based models for the quantitative prognosis of melanoma across the three age groups adults young adults and children
Adaptation and optimization of algorithms to adapt and optimize existing precision medicine algorithms which are currently being utilized in adult patient care for their application within pediatric and young adult populations
Implementation of transfer learning given the limitations associated with pediatric and young adult data we intend to utilize transfer learning techniques The study will employ a sequential waterfall methodology whereby machine learning models trained on adult patient data will be fine-tuned using the more limited data from younger cohorts
Integration of expert medical opinion to integrate physicians39 scientific domain knowledge into the decision support system This will be facilitated through the comprehensive examination of existing literature as well as the evaluation of variable risk contributions within each patient group
AI-based prognostic models to develop artificial intelligence-based models for the quantitative prognosis of melanoma across the three age groups adults young adults and children
Detailed Description:
Precis-Mel 1 is a unicentric observational study using retrospectively collected data The proposed procedure is to start using data including demographic and family data genetic data medical procedures and cancer treatment cutaneous biopsy etc to build a multidimensional dataset and apply AI algorithms that can produce survival curves and sentinel lymph node SLNB positivity in CAYA The approach to be used is presented in the following sub-sections
Data engineering the multidimensional dataset is meticulously integrated via DBT and SQL queries on a PostgreSQL database This results in a model-ready comprehensive table maintaining the crucial temporal dimension of patient istories Identifiers are assigned to maintain the integrity of the data trail and the connection between various patient events such as metastasis and death Python-based transformations ensure that sequential patient events are ontextually enriched by preceding occurrences Operations include arithmetic aggregations extremum calculations and string manipulations Events are discretized over a standardized temporal frame 1-3 months for uniform staging reference also serving to consolidate any misaligned data instances
Model development our approach employs survival analysis to address the unique challenges of our dataset particularly censoring where an event of interest like death does not occur within the observation window Based on our previous experience in modelling this problem we prefer to use Gradient Boosting Survival Analysis GBSA a non-deep learning method as it effectively addresses data scarcity issues GBSA adapts the gradient boosting machine algorithm for survival analysis particularly accommodating censored data In survival analysis patients are represented by a triplet xi δi Ti where xi is the feature vector Ti is the time to event and δi indicates whether the observation is censored Our goal is to estimate the survival function St representing the probability of a patient surviving beyond time t and the hazard function λt indicating the instantaneous probability of an event occurring at time t To adapt it for the survival modelling domain our model utilizes the gradient boosting approach with a modified loss function the negative log partial likelihood This allows us to effectively estimate the survival function
Performance metrics we measure model performance using the concordance index c-index a metric particularly suited for survival analysis The c-index assesses the predictive accuracy of our model by comparing predicted and observed event times A high c-index indicates that our model effectively predicts the order of patient hazard given its input features
Data engineering the multidimensional dataset is meticulously integrated via DBT and SQL queries on a PostgreSQL database This results in a model-ready comprehensive table maintaining the crucial temporal dimension of patient istories Identifiers are assigned to maintain the integrity of the data trail and the connection between various patient events such as metastasis and death Python-based transformations ensure that sequential patient events are ontextually enriched by preceding occurrences Operations include arithmetic aggregations extremum calculations and string manipulations Events are discretized over a standardized temporal frame 1-3 months for uniform staging reference also serving to consolidate any misaligned data instances
Model development our approach employs survival analysis to address the unique challenges of our dataset particularly censoring where an event of interest like death does not occur within the observation window Based on our previous experience in modelling this problem we prefer to use Gradient Boosting Survival Analysis GBSA a non-deep learning method as it effectively addresses data scarcity issues GBSA adapts the gradient boosting machine algorithm for survival analysis particularly accommodating censored data In survival analysis patients are represented by a triplet xi δi Ti where xi is the feature vector Ti is the time to event and δi indicates whether the observation is censored Our goal is to estimate the survival function St representing the probability of a patient surviving beyond time t and the hazard function λt indicating the instantaneous probability of an event occurring at time t To adapt it for the survival modelling domain our model utilizes the gradient boosting approach with a modified loss function the negative log partial likelihood This allows us to effectively estimate the survival function
Performance metrics we measure model performance using the concordance index c-index a metric particularly suited for survival analysis The c-index assesses the predictive accuracy of our model by comparing predicted and observed event times A high c-index indicates that our model effectively predicts the order of patient hazard given its input features
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None