Neoplasms

Digestive System Diseases

All Conditions

Rare Diseases

Stomach Neoplasms

Stomach Cancer

Participants will be tested for CDSS-assisted screening upper gastrointestinal endoscopy

Participants will be tested for Conventional routine screening upper gastrointestinal endoscopy

Expert endoscopists perform screening upper gastrointestinal endoscopy while looking at the monitor (CDSS system: automatic detection or lesion classification function) and listening to the sound of the monitor next to the original endoscopy monitor system.

CDSS-assisted upper GI endoscopy

Expert endoscopists perform routine screening upper gastrointestinal endoscopy without CDSS's help.

no intervention

Chang Seok Bang, MD., PhD.

Chang Seok Bang, MD, PHD

Cho Won Kim, MS

Chang Seok Bang, MD, PhD

This study aimed to find the CDSS-assisted upper GI endoscopy has clinical benefit compared to conventional routine screening upper GI endoscopy. Investigators will conduct a pilot randomized controlled study. Consecutive patients will be allocated either to CDSS-assisted upper GI endoscopy or conventional routine screening upper GI endoscopy. The lesion detection rate will be compared between both groups.

Artificial intelligence has been adopted in the field of gastrointestinal endoscopy. Investigators previously established deep-learning models to predict the histology and invasion depth of gastric lesions using endoscopic stillcut images. However, clinical benefit of this model has not been evaluated. This study aimed to find the CDSS-assisted upper GI endoscopy has clinical benefit compared to conventional routine screening upper GI endoscopy. Investigators will conduct a pilot randomized controlled study. Consecutive patients who visited Chuncheon Sacred Heart hospital will be allocated either to CDSS-assisted upper GI endoscopy or conventional routine screening upper GI endoscopy. The lesion detection rate will be compared between both groups. Expert endoscopists will conduct this randomized study.

CDSS-assisted UGI Endoscopy vs. Routine Screening Endoscopy

Clinical Benefit in the CDSS-assisted Screening Upper Gastrointestinal Endoscopy vs. Routine Screening Endoscopy

Gastric lesions (low or high grade dysplasia, early gastric cancer, or advanced gastric cancer) detection rate

Whether CDSS correctly diagnosed detected lesions or not

Chuncheon Sacred Heart Hospital

Symptoms and General Pathology

Heart and Blood Diseases

Blood and Lymph Conditions

Immune System Diseases

Body Weight

Multiple Myeloma

Neoplasms, Plasma Cell

Neoplasms by Histologic Type

Hemostatic Disorders

Blood Coagulation Disorders

Vascular Diseases

Paraproteinemias

Blood Protein Disorders

Hematologic Diseases

Hemorrhagic Disorders

Lymphoproliferative Disorders

Immunoproliferative Disorders

Machine learning (ML): A mixed data set of 200 WB-MRI scans comprising scans obtained from 40 healthy volunteers (scanned for the purposes of the study), 40 previously acquired inactive myeloma WB-MRI scans and 120 previously acquired active myeloma WB-MRI scans, in which machine learning and convolutional neural networks will be trained to recognise healthy marrow, treated inactive previous myeloma and active myeloma. An algorithm will be developed for testing in phase 2.

Machine Learning (ML): A mixed data set of 353 WB-MRI scans as that comprising 50 healthy volunteers (scanned for the purposes of the study), and previously acquired scans from 303 myeloma patients, 100 of whom have inactive disease and 203 of whom have active myeloma. The scans will be read by radiologists in random order either with or without the support of for the detection of active myeloma. The diagnostic performance of the radiology reads with or without the machine learning support will be measured against an expert panel reference standard.

Machine Learning (ML): Approximately 200 paired WB-MRI scans from 100 patients (scanned at baseline with active disease and then post treatment) will be used to develop a machine learning tool to quantify the burden of disease. The machine learning algorithm will then be tested on a further additional set of 60 patients who previously had two WB-MRI scans comprising paired baseline (with active disease) and post treatment scans. The agreement of radiology readers to evaluate the burden of disease will be measured against the reference standard (expert panel) with and without machine learning support.

Application of ML support algorithm to accelerate and enhance human interpretation of WB-MRI scans in patients with myeloma

Machine Learning (ML)

Andrea G Rockall, FRCR

Christina Messiou, MD, FRCR

Diffusion-weighted Whole Body Magnetic Resonance Imaging (WB-MRI) is a new technique that builds on existing Magnetic Resonance Imaging (MRI) technology. It uses the movement of water molecules in human tissue to define with great accuracy cancerous cells from normal cells. Using this technique the investigators can much more accurately define the spread and rate of cancer growth. This information is vital in the selection of patients' treatment pathways. WB-MRI images are obtained for the entire body in a single scan. Unlike other imaging techniques such as computed Tomography (CT) or Positron Emission Tomography (PET) PET/CT there is no radiation exposure.

Despite the considerable advantages that this new technique brings, including "at a glance" assessment of the extent of disease status, WB-MRI requires a significant increase in the time required to interpret one scan. This is because one whole body scan typically comprises several thousand images. Machine learning (ML) is a computer technique in which computers can be 'trained' to rapidly pin-point sites of disease and thus aid the radiologist's expert interpretation. If, as the investigators believe, this technique will help the radiologist to interpret scans of patients with myeloma more accurately and quickly, it could be more widely adopted by the NHS and benefit patient care.

The investigators will conduct a three-phase research plan in which ML software will be developed and tested with the aim of achieving more rapid and accurate interpretation of WB-MRI scans in myeloma patients.

Rationale:

Diffusion-weighted whole body magnetic resonance imaging (WB-MRI) is a technique that depicts myeloma deposits in the bone marrow. WB-MRI covers the entire body during the course of a single scan and can be used to detect sites of disease without using ionising radiation. Although WB-MRI allows for "at a glance" assessment of disease burden, it requires significant expertise to accurately identify and quantify active myeloma. The technique is time-consuming to report due to the great number of images. A further challenge is recognising whether a patient has residual disease after treatment. Machine learning (ML) is a computer technique that can be trained to automatically detect disease sites in order to support the radiologist's interpretation. The investigators believe this technique will help the radiologist to interpret the scan more accurately and quickly.

Machine learning algorithms have been successfully developed to recognise some other cancer types. The investigators believe that it may be successful in patients with myeloma, in whom The National Institute for Health and Care Excellence (NICE) recommend whole body MRI. This could allow the technique to be more widely used in the National Health Service (NHS). In the MALIMAR study the investigators will develop and test ML methods that have the potential to increase accuracy and reduce reading time of WB-MRI scans in myeloma patients. The investigators propose to develop ML tools to detect and quantify active disease before and after treatment based on WB-MRI.

Research will be carried out at the Royal Marsden Hospital (RMH) NHS Foundation Trust, Institute of Cancer Research (ICR) London and Imperial College London. The investigators will use Whole Body MRI (WB-MRI) scans that have already been acquired in myeloma patients. They will also include 50 new scans obtained at RMH from healthy volunteer scans which will be used to 'teach' the computer to distinguish between healthy and diseased tissues.

Research Design:

The research will be divided into three parts:

1. Development of the Machine Learning (ML) tool to detect active myeloma
2. Measurement of the ability of the ML tool to improve the radiologists' interpretation of WB-MRI scans using a set of scans from patients with active and inactive myeloma and new scans obtained from healthy volunteers
3. Development of the ML tool to quantify disease burden and changes between pre- and post-treatment WB-MRI scans in order to identify response to treatment

The main outcome measure for this study will be the improvement in the detection of active disease and disease burden and the reduction in radiology reading time. The investigators will assess the reduction in reading time in both experienced specialist and non-specialist radiologists.

Cross-sectional diagnostic test accuracy design: development of a machine-based algorithm to augment expert classification of disease status and response to treatment in myeloma patients using retrospective interpretation of WB-MRI scans and disease-free (healthy volunteers) for comparison purposes.

The assessors interpretation of disease status using WB-MRI scans will be fully blinded to the reference standard (i.e. the Expert Panel's interpretation of the same scan).

Machine Learning in Myeloma Response

Development of a Machine Learning Support for Reading Whole Body Diffusion Weighted Magnetic Resonance Imaging (WB-DW-MRI) in Myeloma for the Detection and Quantification of the Extent of Disease Before and After Treatment

Sensitivity for the detection of active myeloma on WB-MRI with and without ML support versus the reference standard

Agreement between readers and reference standard in scoring overall disease burden with and without ML intervention

Agreement of machine learning algorithm with reference standard to classify disease spread assessed as percentage accuracy

Per site sensitivity to diagnose active disease

Difference in reading time assessed in minutes

Per site specificity to diagnose active disease

Institute of Cancer Research, United Kingdom

Imperial College London

Royal Marsden NHS Foundation Trust

Carcinoma

Liver Cirrhosis

Carcinoma, Hepatocellular

Liver Diseases

Neoplasms, Glandular and Epithelial

Adenocarcinoma

Liver Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Chronic Graft Versus Host Disease

Patients with hepatocellular carcinoma greater than 1 cm in size. All patients from an ultrasound screening programme who have been diagnosed with a nodule larger than 1 cm and referred to our centres will be included in this group. They will then be excluded of this group if the diagnosis of hepatocellular carcinoma is not retained according to the radiological or histological reference diagnostic standards (gold standard).

Patients without hepatocellular carcinoma. A 1-year interval ultrasound will be performed to confirm the absence of new nodule in the year following inclusion.

One to three video acquisitions of 10 seconds will be carried out via the intercostal route. Data acquisition will be standardized according to a mandatory protocol and previously recorded in each ultrasound machine (cross shots, harmonic, filter, depth, focal length, mechanical index, etc.).

Video acquisition

Armelle TAKEDA, PhD

Anita PAISANT

Clémence CANIVET

Olivier SEROR

Pierre NAHON

Riccardo SARTORIS

Pierre-Emmanuel RAUTOU

Laurent MILOT

Agnès RODE

Philippe MERLE

Christophe CASSINOTTO

José URSIC-BEDOYA

Jérémy DANA, MD

By 2030, hepatocellular carcinoma (HCC) will become the second leading cause of cancer-related death, accounting for more than one million deaths per year according to the World Health Organization.

To this date, screening for hepatocellular carcinoma in France remains uniform for all patients, based solely on a liver ultrasound every 6 months. This strategy has three main limitations: lack of personalisation, low compliance, relatively poor performance of the ultrasound.

Risk stratification models have been developed for chronic hepatitis C, alcoholic cirrhosis and non-alcoholic steatohepatitis (NASH) including clinical and biological parameters but no analysis of the liver parenchyma which is the physiopathological substrate of hepatocarcinogenesis.

The advent of new artificial intelligence techniques could revolutionize the approach and lead to a personalised radiological screening strategy.

Deep learning, a subclass of machine learning, is a popular area of research that can help humans performing certain tasks by automatically identifying new image features not defined by humans.

The hypothesis of this study is that the non-tumor cirrhotic liver parenchyma is rich in structural information reflecting the severity of the hepatopathy, its carcinological risk and the process of hepatocarcinogenesis. Its analysis combined with clinical and biological data, which have already been studied to stratify the risk of hepatocarcinogenesis, will allow to define a very high-risk population, particularly in the context of Hepatitis C Virus (HCV) eradication and Hepatitis B Virus (HBV) control.

Consequently, this study proposes to design prospectively a deep learning model for stratification of the risk of hepatocarcinogenesis by including clinical, biological and radiological ultrasound parameters.

By 2030, hepatocellular carcinoma (HCC) will become the second leading cause of cancer-related death, accounting for more than one million deaths per year according to the World Health Organization.

To this date, screening for hepatocellular carcinoma in France remains uniform for all patients, based solely on a liver ultrasound every 6 months. This scheme has the advantage of associating an acceptable cost-effectiveness ratio and, above all, of obtaining an increased overall survival. However, this strategy has three main limitations: lack of personalisation, low compliance, relatively poor performance of the ultrasound.

Risk stratification models have been developed for chronic hepatitis C, alcoholic cirrhosis and non-alcoholic steatohepatitis (NASH) including clinical (age, sex, body mass index and diabetes) and biological (ASAT/ALAT, platelets, albumin) parameters. However, they didn't include analysis of the liver parenchyma which is the physiopathological substrate of hepatocarcinogenesis. In the 1990s, several authors studied the incidence of hepatocellular carcinoma according to the liver echostructure. They agreed on the over-risk represented by a nodular heterogeneous echostructure with an estimated rate ratio of up to 20.

However, all these results have not yet led to a personalised radiological screening strategy. The advent of new artificial intelligence techniques could revolutionize the approach.

Deep learning, a subclass of machine learning, is a popular area of research that can help humans performing certain tasks. Unlike radiomics, deep learning can automatically identify new image features not defined by humans.

The hypothesis of this study is that the non-tumor cirrhotic liver parenchyma is rich in structural information reflecting the severity of the hepatopathy, its carcinological risk and the process of hepatocarcinogenesis. Its analysis combined with clinical and biological data, which have already been studied to stratify the risk of hepatocarcinogenesis, will allow to define a very high-risk population, particularly in the context of Hepatitis C Virus (HCV) eradication and Hepatitis B Virus (HBV) control.

Consequently, this study proposes to design prospectively a deep learning model for stratification of the risk of hepatocarcinogenesis by including clinical, biological and radiological ultrasound parameters. The primary objective of the study is to identify a population at very high risk of developing hepatocarcinoma in order to propose different screening modalities to the patients most at risk.

This clinical study will include patients aged over 18 years referred by their hepatologist in the framework of ultrasound screening according to the European Association for the Study of the Liver (EASL) recommendations for hepatocellular carcinoma screening, except for non-cirrhotic HBV liver disease: non-cirrhotic F3-stage liver disease from any cause based on individual risk assessment for hepatocarcinoma; cirrhosis from any cause, non-viral or virologically cured (HCV) or controlled (HBV). Patients with a history of treated hepatocellular carcinoma will be excluded.

Two groups of patients will be constituted prospectively: group 1 will include patients with a diagnosis of hepatocellular carcinoma greater than 1 cm (reference diagnostic standards: radiological or histological). These patients will therefore correspond to a very high-risk; Group 2 will include patients without hepatocellular carcinoma, thus corresponding to a lower risk. A 1 year-interval ultrasound will be performed in patients of group 2 to confirm the absence of new nodule in the year following inclusion. The proportion of new hepatocellular carcinoma should not exceed 3%.

The data collected will be clinical, biological, elastographic and ultrasonic parameters.

A Deep Learning model using a deep convolutional neural network architecture will be developed on Python using these data.

On a total of 7 investigation sites, 300 patients (equitably distributed between the two groups) will be included in the training/validation cohort and 100 patients (equitably distributed between the two groups) in the test cohort. These numbers are calculated from ultrasound studies reporting a rate ratio of HCC risk of up to 20 in case of macronodular ultrasound pattern and Deep Learning requirements (large numbers needed).

The training/validation and test cohorts will be from external and independent centres.

The diagnostic performance of the model will be estimated by Area Under the Curve (AUC), sensitivity, specificity and F1-score (95% confidence intervals) on the test cohort.

Risk Stratification of Hepatocarcinogenesis Using a Deep Learning Based Clinical, Biological and Ultrasound Model in High-risk Patients

Deep Learning-based cross-analysis of clinical, biological, elastographic and ultrasonic (non-tumor liver parenchyma) parameters

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
CDSS-assisted UGI Endoscopy vs. Routine Screening Endoscopy	NCT05452473	Healthy Individ...	CDSS-assisted u... no intervention	20 Years -	Chuncheon Sacred Heart Hospital
Machine Learning in Myeloma Response	NCT03574454	Myeloma	Machine Learnin...	40 Years - 100 Years	Royal Marsden NHS Foundation Trust
Machine Learning in Myeloma Response	NCT03574454	Myeloma	Machine Learnin...	40 Years - 100 Years	Royal Marsden NHS Foundation Trust
Risk Stratification of Hepatocarcinogenesis Using a Deep Learning Based Clinical, Biological and Ultrasound Model in High-risk Patients	NCT04802954	Hepatocellular ... Chronic Liver D...	Video acquisiti...	18 Years -	IHU Strasbourg
Risk Stratification of Hepatocarcinogenesis Using a Deep Learning Based Clinical, Biological and Ultrasound Model in High-risk Patients	NCT04802954	Hepatocellular ... Chronic Liver D...	Video acquisiti...	18 Years -	IHU Strasbourg
CDSS-assisted UGI Endoscopy vs. Routine Screening Endoscopy	NCT05452473	Healthy Individ...	CDSS-assisted u... no intervention	20 Years -	Chuncheon Sacred Heart Hospital

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