Symptoms and General Pathology

All Conditions

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Rare 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

Acute Graft Versus Host Disease

machine learning directed identification of chemotherapy patients at high-risk for emergency department acute care and/or hospitalization

Machine learning algorithm

Manisha Palta, MD

The objective of this study is to apply a validated machine-learning based model (SHIELD-RT, NCT04277650) to a cohort of patients undergoing systemic therapy as outpatient cancer treatment to generate an automatic system for the prediction of unplanned hospital admission rates and emergency department encounters.

A previously described machine learning (ML)-based model accurately predicted ED visits or hospitalizations for cancer patients undergoing radiation therapy or chemoradiation. An IRB approved prospective randomized trial, SHIELD-RT (NCT04277650) found that preemptive intervention for patients undergoing radiation and chemoradiation based on the ML model's risk stratification decreased the relative risk of acute care visits by 50%, showing that ML-guided escalation of care improved personalized supportive care and treatment compliance while decreasing healthcare costs.

The objective of this study is to apply this validated ML based model to a cohort of patients undergoing systemic therapy as outpatient cancer treatment to generate an automatic system for the prediction of unplanned hospital admission rates and emergency department encounters. Once validated, this study will add to the previously published body of evidence supporting a randomized trial evaluating the ML algorithm's ability to assign intervention for patients receiving systemic therapy at highest risk for acute care encounters.

Machine Learning to Predict Acute Care During Cancer Therapy

Generalizable Machine Learning to Predict Acute Care During Outpatient Systemic Cancer

University of California, San Francisco

Duke University

Respiratory Tract (Lung and Bronchial) Diseases

Lung Neoplasms

Thoracic Diseases

Lakisha Marshall

Jenny Messenger

Anna Bibby

Ruth Sharrock

George Tsaknis

Martin Ledson

Richard Lee

Liz Fuller

Cyrus Daneshvar

Imran Hussain

Nadia Elkaram

The study will use a blood sample collected from participants to:

* Develop new ways of finding and diagnosing lung health problems, such as lung cancer.
* Develop tools which make it easier to screen people with possible lung health problems, diagnose problems earlier and with fewer tests, and start the best treatment faster.
* Help improve the early diagnosis of lung cancer, as finding lung cancer early means that it can be treated more easily and successfully.

The results from this study will be linked with the data from the DART study (also collecting data through the Lung Health Check programme) to develop new ways of using computer technology (artificial intelligence) to improve lung health care. The studies use computer programs (called 'algorithms') which can be trained to analyse medical samples. Once developed, these algorithms can be used to support doctors by increasing their speed and accuracy of diagnosing issues.

30ml blood sample will be collected and processed, resulting in plasma and serum samples for analysis.

SCOOT: Sample Collection for DART

Sample Collection for The Integration and Analysis of Data Using Artificial Intelligence to Improve Patient Outcomes With Thoracic Diseases

The data sharing plans for the current study are unknown and will be made available at a later date.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Machine Learning in Myeloma Response	NCT03574454	Myeloma	Machine Learnin...	40 Years - 100 Years	Royal Marsden NHS Foundation Trust
Machine Learning to Predict Acute Care During Cancer Therapy	NCT05122247	Chemotherapeuti...	Machine learnin...	18 Years -	Duke University
Machine Learning in Myeloma Response	NCT03574454	Myeloma	Machine Learnin...	40 Years - 100 Years	Royal Marsden NHS Foundation Trust
SCOOT: Sample Collection for DART	NCT05368298	Lung Cancer		55 Years - 75 Years	University of Oxford
SCOOT: Sample Collection for DART	NCT05368298	Lung Cancer		55 Years - 75 Years	University of Oxford

Spots Global Cancer Trial Database for algorithm

Every month we try and update this database with for algorithm cancer trials from around the world to help patients and their families find trials that might be right for them.
We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.

Spots Global Cancer Trial Database for algorithm

Every month we try and update this database with for algorithm cancer trials from around the world to help patients and their families find trials that might be right for them. We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.

Every month we try and update this database with for algorithm cancer trials from around the world to help patients and their families find trials that might be right for them.
We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.