Neoplasms

All Conditions

Head and Neck Neoplasms

All Drugs and Chemicals

Immunomodulating Agents

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 clinical characteristics and treatments and to compare OS in locally advanced HNSCC patients

To describe the epidemiologic and clinical characteristics, and treatment and the impact of introducing immunotherapy in recurrent or metastatic HNSCC

All the groups will be descriptive, there is not intervention, as it is an Observational study applying artificial Intelligence (RWE).

No intervention - Just description and predictive models

Miren Taberna, MD,PhD

Miren Taberna

John Almeida

Sujith Baliaga

David Casadevall

Melvin Chua

Andreas Dietz

Robert Ferris

Raul Giglio

Chris Holsinger

Kate Hutcheson

Husham Menhanna

Pablo Parente

Sandro Porceddu

Christian Simon

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.

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.

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

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

To describe the demographics, clinical characteristics, and treatment of patients with nasopharynx, paranasal sinus, and salivary gland tumors.

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

* 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

* 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.

Head and Neck Cancer International Group (HNCIG)

Savana Research

Skin and Connective Tissue Diseases

Breast Neoplasms

Breast Diseases

Skin Diseases

women over the age of 18 with breast cancer or remission

non-inferiority study, randomized, blind, excluding health product, with no risk or constraint, in which all the acts are performed in the usual way and do not modify the patient's management.

survey

Chatbots (contraction of "chat" and "robot") are a computer software program that use statistical learning and aim to simulate a conversation by text or voice message. The use of these chatbots was evaluated as part of the increase in therapeutic adherence and information on well-being and mental health.

In clinical studies, chatbots are useful in gathering information through questionnaires directly submitted during conversations or through keyword analyzes. There is no longer any delay between the moment the patient notices a side effect, for example, and the moment when the patient announces it during a consultation. The responses given by the chatbot do not constitute medical advice and only provide information on the treatments envisaged or in progress in the context of the management of breast cancer.

The main objective of the study is to show that the chatbot Vik's answers to the common questions asked by breast cancer patients about their therapeutic management are at least as satisfactory as answers given by a committee of multidisciplinary doctors

A total of 20 most frequently asked questions will be defined on a cohort of 150 breast cancer patients. These questions will be asked on the one hand to the chatbot Vik and on the other hand to a multidisciplinary medical committee (surgeon oncologist, medical oncologist and oncologist radiotherapist). The scientific quality of the responses of the two groups will be validated by a second multidisciplinary group of physicians, independent of the first.

A cohort of 150 patients will then be randomized blind and in 1 to 1 to receive the responses of the chatbot Vik and the answers of the doctors' committee. Each patient will then have to answer the EORTC QLQ-INFO255-7 questionnaire, evaluating the quality of the medical information.

Artificial Intelligence vs Physicians for Breast Cancer Patients' Information

Artificial Intelligence vs Physicians for Breast Cancer Patients' Information : a Blind, Randomized, Controlled Study

The quality of the participating patient's information about their disease and their treatment will be assessed using the standardized EORTC QLQ-INFO25 survey.

Wefight

Symptoms and General Pathology

Digestive System Diseases

Rare Diseases

Neoplasm Metastasis

Carcinoma

Carcinoma, Hepatocellular

Cholangiocarcinoma

Klatskin Tumor

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

Adenocarcinoma

Liver Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Liver Diseases

Bile Duct Cancer

Gallbladder Cancer

Intrahepatic Cholangiocarcinoma

Fibrolamellar Carcinoma

Participants' cases are discussed in tumor conference without the use of ADBoard, according to conventional practice.

Participants' cases will be evaluated by ADBoard directly before they are discussed in the tumor conference.

Utilizing the clinical decision support system 'ADBoard' for therapy selection in participants with hepatobiliary tumors

ADBoard

Felix Krenzien

Christian Benzing

The goal of this observational study is to compare the recommendations of the artificial intelligence clinical decision support system 'ADBoard', with the recommendations of physicians by tumor conferences in patients with hepatobiliary tumors. The main questions it aims to answer are:

Can ADBoard achieve a high level of similar recommendations as physicians' tumor conferences? Can ADBoard consider a more complete set of patient-related data than in physicians' tumor conferences? Can ADBoard reduce the time between the first time the patient is discussed at the tumor conference and the start of the recommended treatment plan? Participants will have their hepatobiliary tumor treatments determined by either tumor conference with ADBoard, or tumor conference without ADBoard.

The ADBoard study is a monocentric, prospective, parallel randomized controlled trial with a non-inferiority framework. Participants will be randomized 1:1 into one of two groups: either a) tumor conference with ADBoard, or b) tumor conference without ADBoard.

Therapeutic Assistance and Decision-making Algorithms in Hepatobiliary Tumor Boards

Evaluation of the Trustworthiness of the Application of Artificial Intelligence and Decision Support Systems for the Creation of Tumor Conference Protocols

Examples of possible treatment recommendations are liver resection (curative), regimen of chemotherapy, immunotherapy, radiotherapy, transarterial chemoembolisation, diagnostics, best-supportive care, follow-up by re-presentation.

The interrater reliability of the recommendations of ADBoard and the tumor conferences with regard to their agreement will be measured.

The intrarater reliability will be measured by testing all participant cases several times by the ADBoard according to the required sample size with sufficient statistical power (test-retest). Interrater and intrarater reliability will be evaluated descriptively (percentage of agreement, contingency tables), and finally the Cohen-Kappa value will be assessed.

For each type of tumor examined, a series of relevant parameters is defined by the medical staff. The objective is achieved if complete documentation, defined as documentation status 'present' or 'detected as missing', is present in more than or equal to 75% of all ADBoard-assisted decisions.

The System Causability Scale will be reviewed by 3 specialist physicians. The score ranges from 0 to 1. A higher score means greater suitability of the user interface, explanation, or explanation process itself for the intended purpose of tumor conference treatment recommendations.

The time between the initial presentation of the case to the tumor conference and the time of recommendation implementation will be recorded.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Towards A Better Paradigm for Head and Neck Cancer Treatment Applying Artificial Intelligence. HNC-TACTIC.	NCT05117775	Head and Neck C...	No intervention...	18 Years -	Savana Research
Artificial Intelligence vs Physicians for Breast Cancer Patients' Information	NCT03556813	Breast Cancer	survey	18 Years -	Wefight
Towards A Better Paradigm for Head and Neck Cancer Treatment Applying Artificial Intelligence. HNC-TACTIC.	NCT05117775	Head and Neck C...	No intervention...	18 Years -	Savana Research
Therapeutic Assistance and Decision-making Algorithms in Hepatobiliary Tumor Boards	NCT05681949	Hepatocellular ... Fibrolamellar H... Cholangiocarcin... Intrahepatic Ch... Metastasis to L... Gallbladder Car...	ADBoard	18 Years -	Charite University, Berlin, Germany
Artificial Intelligence vs Physicians for Breast Cancer Patients' Information	NCT03556813	Breast Cancer	survey	18 Years -	Wefight
Therapeutic Assistance and Decision-making Algorithms in Hepatobiliary Tumor Boards	NCT05681949	Hepatocellular ... Fibrolamellar H... Cholangiocarcin... Intrahepatic Ch... Metastasis to L... Gallbladder Car...	ADBoard	18 Years -	Charite University, Berlin, Germany

Spots Global Cancer Trial Database for natural language processing