Towards A Better Paradigm for Head and Neck Cancer Treatment Applying Artificial Intelligence. HNC-TACTIC.

Head and Neck Cancer Clinical Trial

— HNC-TACTIC  

Official title:

Towards A Better Paradigm for Head and Neck Cancer Treatment Applying Artificial Intelligence: an International Cohort Study of Electronic Health Records. HNC-TACTIC.

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05117775
• Other study ID #: HNC-TACTIC
• Status: Recruiting
• Start date: December 6, 2021
• Est. completion date: December 31, 2024

Study information

• Verified date: November 2023
• Source: Savana Research
• Contact: Miren Taberna, MD,PhD
• Phone: [+34] 910 69 69 02
• Email: mtaberna[@]savanamed.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

This will be an international, multicenter, retrospective, observational, and data-driven study using secondary data captured in EHRs. The extraction of the data captured in the EHRs will be performed with SAVANA's EHRead®, an innovative data-driven system based on Natural Language Processing (NLP) and machine learning. For all patients, the Index Date is defined as the timepoint within the study period when they fulfill ALL inclusion criteria and no exclusion criteria. Follow-up comprises the period between Index Date and the last EHR available within the study period. Additional variable-specific time windows may be considered to optimize data collection.

Description:

The present study aims to describe the clinical characteristics of patients with HNSCC in a real-world setting by analyzing readily available information in the Electronic Health Records (EHRs). This study will gain a deep insight of the clinical characteristics and real-world outcomes of patients with all stages (early, locally advanced, and metastatic) of HNSCC. It will focus on developing two predictive models to apply in the clinical setting, one for electing patients with high-risk of recurrence after radical treatment, and the second one for selecting recurrent or metastatic patients who could benefit from immunotherapy.

To achieve the proposed study objectives we will use SAVANA´s EHRead® (11-15), a technology that applies Natural Language Processing (NLP) (16) and machine learning to extract, organize, and analyze the unstructured clinical information jotted down by health professionals in patients' EHRs.

Primary objectives

• To develop a predictive model based on dynamic risk stratification (DRS) for the risk of recurrence or disease progression following a primary curative treatment in HNSCC patients with early and locally advanced disease.

• To develop a predictive model based on dynamic risk stratification (DRS) aimed at identifying patients' features that predict long-term survival after immunotherapy in recurrent and metastatic HNSCC patients. Secondary objectives

• To describe median OS by primary tumor location (oral cavity, oropharynx, larynx, and hypopharynx) in HNSCC patients after stratification for prognostic factors, including tumor stage and treatment.

• To describe the demographics, clinical characteristics, and treatment of patients with HNSCC in early and locally advanced stages of the disease.

• To describe the patterns of follow-up in patients with HNSCC in early and locally advanced stages of the disease.

• Departments in charge

• Number of visits

• Imaging and anatomopathological tests

• Recurrence detected by physical examination.

• To evaluate the impact of treatments on patients with locally advanced stages of the disease.

• Patients' early and late toxicity to the treatment, comparing between radiotherapy (+/-cisplatin or cetuximab) vs surgery and post-operative r< radiotherapy (+/- cisplatin).

• Healthcare resource utilization (HCRU), including medical visits, diagnostics, and hospitalizations.

• To compare OS in locally advanced HNSCC patients (including both HPV+ and HPV- oropharyngeal patients) treated with cisplatin-radiotherapy vs cetuximab-radiotherapy and treated with surgery vs. conservative treatment.

• To compare the demographic and clinical characteristics of exceptional responders and poor responders (based on recurrence and long-term survival). This analysis will be performed independently for HPV+ and HPV- oropharyngeal patients.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 10000
• Est. completion date: December 31, 2024
• Est. primary completion date: February 28, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Patients =18 years old.

• Patients diagnosed with HNSCC

• For selected exploratory analyses, patients diagnosed with nasopharynx, paranasal sinus, and salivary gland tumors.

Exclusion Criteria:

• Patients with follow-up of less than 6 months, except if deceased (any cause) in the 6 months after HNSCC diagnosis

Related Conditions & MeSH terms

• Head and Neck Cancer
• Head and Neck Neoplasms

Intervention

Other:
• No intervention - Just description and predictive models
All the groups will be descriptive, there is not intervention, as it is an Observational study applying artificial Intelligence (RWE).

Locations

Country	Name	City	State
Spain	Savan Research S.L	Madrid

Sponsors (2)

Lead Sponsor	Collaborator
Savana Research	Head and Neck Cancer International Group (HNCIG)

Country where clinical trial is conducted

Spain, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Exploratory objective	To describe the demographics, clinical characteristics, and treatment of patients with nasopharynx, paranasal sinus, and salivary gland tumors.	From 1st Jan 2021
Primary	Predictive model based on dynamic risk stratification (DRS) for the risk of recurrence or disease progression	To develop a predictive model based on dynamic risk stratification (DRS) for the risk of recurrence or disease progression following a primary curative treatment in HNSCC patients with early and locally advanced disease.	From 1st Jan 2021
Primary	Predictive model based on dynamic risk stratification (DRS) aimed at identifying patients' features	To develop a predictive model based on dynamic risk stratification (DRS) aimed at identifying patients' features that predict long-term survival after immunotherapy in recurrent and metastatic HNSCC patients	From 1st Jan 2021
Secondary	In all patients with all stages of HNSCC (full analysis set, FAS):	To describe median OS by primary tumor location (oral cavity, oropharynx, larynx, and hypopharynx) in HNSCC patients after stratification for prognostic factors, including tumor stage and treatment.	From 1st Jan 2021
Secondary	In patients with early and locally advanced stages of the disease (including all patients treated with curative intent):	To describe the demographics, clinical characteristics, and treatments; To describe the patterns of follow-up; Departments in charge; Number of visits; Imaging and anatomopathological tests; Recurrence detected by physical examination; To evaluate the impact of treatments on patients with locally advanced stages of the disease.; Patients' early and late toxicity to the treatment, comparing between radiotherapy (cisplatin or cetuximab) vs surgery and post-operative radiotherapy (cisplatin); Healthcare resource utilization (HCRU), including medical visits, diagnostics, and hospitalizations.; To compare OS in locally advanced HNSCC patients (including both HPV+ and HPV- oropharyngeal patients) treated with cisplati	From 1st Jan 2021
Secondary	In patients with recurrent or metastatic disease:	To describe the epidemiologic and clinical characteristics, and treatments; To describe epidemiologic, clinical, and tumor characteristics of long-term survivors treated with immunotherapy and non-immunotherapy approaches.; To describe the impact of introducing immunotherapy in recurrent or metastatic; HNSCC: Treatment outcome: OS; HCRU, including medical visits, diagnostics, and hospitalizations.	From 1st Jan 2021