Clinical trial · Observational
Non-Invasive Artificial Intelligence-Based Platform MonIToring Program (NIP IT!)
NCT05196087CI-TRIAL-00091663NIP IT!recruitingClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 29, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260929-000001
Summary
Brief summary (as posted)
Patients who have undergone curative treatment may be at risk of relapse. This study will collect, annotate, and sequence biospecimens (blood, stool, and tissue) from patients across different tumor types to detect molecular residual disease (MRD) before metastases become radiographically or clinically detectable. This will allow for early cancer interception, and hopefully prolong relapse-free survival across tumor types.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
| Gastrointestinal Neuroendocrine Tumor | Digestive System Neuroendocrine Tumor | ALIAS | 0.90 |
| Melanoma | Melanoma | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (0)
Data not yet available
No intervention recorded.
Design
Arms and outcomes
Arms (1)
- label
- NIP IT!
- description
- Patients with early stage or locally advanced disease that is planned for or have undergone curative treatment will have next-generation sequencing (NGS)-based ctDNA analysis performed on blood samples to determine minimal residual disease (MRD). Blood samples, stool samples, and additional archival/fresh tumor specimens will be collected for banking and future research purposes.
Primary outcomes (1)
- measure
- Change from Baseline in ctDNA collected from biospecimens
- timeFrame
- Through study completion, an average of 4 years
- description
- Next-generation sequencing based ctDNA analysis
Secondary outcomes (1)
- measure
- Number of participants that are identified as high risk of clinical relapse with artificial intelligence (AI) and machine learning algorithms
- timeFrame
- Through study completion, an average of 4 years
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: 1. Patients with histological confirmation of a solid tumor. 2. Patients must have early stage or locally advanced disease that is planned for or have undergone curative treatment. 3. Patient must be ≥ 18 years old. 4. All patients must have signed and dated an informed consent form. Exclusion Criteria: None
References
Publications (0)
Data not yet available
No reference posted for this study.