Neoplasms

Skin and Connective Tissue Diseases

All Conditions

Breast Neoplasms

Breast Diseases

Skin Diseases

Vasoconstrictor Agents

Respiratory System Agents

All Drugs and Chemicals

Epinephrine

Racepinephrine

Epinephryl borate

Digital breast tomosynthesis plus synthesized 2D mammograms

DBT+s2D

2D-FFDM

Walter Heindel, MD, PhD

This study is a randomized, multicenter, multivendor, controlled, diagnostic superiority trial to compare digital breast tomosynthesis plus synthesized 2D mammograms (DBT+s2D) versus standard 2D full-field digital mammography (2D-FFDM) regarding the effectiveness as screening modality.

The primary objective of the study is to evaluate whether digital breast tomosynthesis plus synthesized 2D mammograms leads to a relevant increase in the detection rate of screening-detected invasive cancers compared to 2D full-field digital mammography in routine screening according to the European Guidelines. Furthermore, the incidence rate of interval cancers within a 24 months interval after screening will be compared between both study arms in order to investigate the potential for overdiagnosis.

According to the pre-defined order of both primary endpoints and the primary objective of the study in the planning phase, the initial sample size calculation was based solely on the first primary endpoint (invasive breast cancer detection rate). Given the increasing national and international attention of interval cancers to assess the impact of potential overdiagnosis caused by tomosynthesis, we have planned a sample size increase from 80,000 to 120,000 study participants to achieve a reasonable statistical power for the evaluation of both primary endpoints. The revised sample size calculation was carried out without knowledge of the data from the currently recruiting TOSYMA study, i.e. all planning assumptions were based on external data that do not belong to the ongoing study.

Participants will be randomized to either Digital Breast Tomosynthesis plus synthesized 2D mammograms (DBT+s2D) or 2D Full-Field Digital Mammography (2D-FFDM)

Breast Cancer Screening: Digital Breast Tomosynthesis Versus Digital 2D Mammography

Prospective Randomized Comparison of Digital Breast Tomosynthesis Plus Synthesized Images Versus Standard Full-field Digital Mammography in Population-based Screening (TOSYMA)

Number of women with screening-detected invasive breast cancer divided by the number of all women screened. A screening-detected breast cancer is classified as invasive carcinoma if the pT category (pathological tumor size) of the TNM classification falls into one of the following categories: pT1mic, pT1a, pT1b, pT1c, pT1, pT2, pT3, pT4a, pT4b, pT4c, pT4d, pT4, pTX (for evaluation purpose pTX defines histologically approved invasive breast cancer with missing tumor diameter) or the final pathological categorization has been done after neoadjuvant therapy (ypT), implying an invasive cancer prior to therapy.

The 24 months incidence of interval cancers is defined as the number of women that develop a ductal carcinoma in situ or an invasive breast cancer in the 24 months interval after a negative screening examination divided by the number of all women with a negative screening result.

Number of women with screening-detected ductal carcinoma in situ (if the pT category of the TNM classification falls into the category pTis) divided by the number of all women screened.

Number of women with screening-detected invasive breast cancers of the category pT1 divided by the number of all women screened. A screening-detected breast cancer is classified as breast cancer of tumor category pT1 if tumor size is ≤ 20 mm in greatest dimension and the respective pT subcategory of the pTNM classification is one of the following: pT1mic, pT1a, pT1b, pT1c, pT1.

Number of women with recalls for further assessment divided by the number of all women screened.

Number of women with screening-detected malignancies (ductal carcinoma in situ or invasive breast cancer) divided by the number of women with recalls for further assessment.

The 12 months incidence of interval cancers is defined as the number of women that develop a ductal carcinoma in situ or an invasive breast cancer in the 12 months interval after a negative screening examination divided by the number of all women with a negative screening result.

German Research Foundation

University Hospital Muenster

Symptoms and General Pathology

Heart and Blood Diseases

Blood and Lymph Conditions

Immune System Diseases

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

Skin Neoplasms

The patients who will have a biopsy of skin suspected to be a melanoma, basal cell carcinoma or squamous cell carcinoma will have a skin imaging with a new Microscopy Optical Coherence (OCTAV)

Control group (patients without skin cancer) will have a skin imaging with a new Microscopy Optical Coherence (OCTAV)

In vivo skin imaging performed by placing the tip of the OCTAV device directly in slight contact with the skin of the patient.

OCTAV Patient

OCTAV Control group

Jean-Luc PERROT, MD

OCTAV is a medical device class I, CE marked, based on a new technique for high-resolution imager (cell) internal microstructures of all types of biological tissues in vivo or ex vivo, to a depth of penetration 800 .mu.m. It allows to explore the epidermis, the dermo-epidermal junction and middle dermis in a totally non-invasive (direct contact with the tissue without sampling).

At the coming of the patient in Dermatology for resection / biopsy of the tumor (usual practice), for the study, an image of the tumor will be performed with the OCTAV device (about 5 minutes) for the study. Then, according to usual practice, resection / biopsy will be performed.

This study aims to validate the clinical relevance of this device to characterize the 3 main skin cancers and to obtain the necessary data for the implementation of future clinical trials.

One group of skin cancer patients One group without skin cancer patients

Optical Coherence Microscopy in Dermato-oncology

In Vivo Imaging Innovation : Optical Coherence Microscopy in Dermato-oncology

Sensitivity measures the proportion of positive skin cancers (according to the OCTAV device) that are correctly identified as such (according to gold standard : histology)

Specificity measures the proportion of negative skin cancers (according to the OCTAV device) that are correctly identified as such (according to gold standard : histology)

Only for the non skin cancer group. Theses measures will identify the different skin layers (Epidermis, Dermis, Subcutaneous tissue, Cross-section)

DAMAE Médical

Centre Hospitalier Universitaire de Saint Etienne

Cécile DUPONT, PhD

OCTAV is a medical device class I, not CE marked, based on a new technique for high-resolution imager (cell) internal microstructures of all types of biological tissues in vivo or ex vivo, to a depth of penetration 800 .mu.m. It allows to explore the epidermis, the dermo-epidermal junction and middle dermis in a totally non-invasive (direct contact with the tissue without sampling).

One group of patients One group of non skin cancer patients

Infections

Urinary Tract, Sexual Organs, and Pregnancy Conditions

Digestive System Diseases

Carcinoma

Carcinoma, Squamous Cell

Communicable Diseases

Acquired Immunodeficiency Syndrome

HIV Infections

Anus Neoplasms

Rectal Neoplasms

Colorectal Neoplasms

Intestinal Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Intestinal Diseases

Anus Diseases

Rectal Diseases

Anal Cancer

HIV-infected male patients who refer to conducting sexual risk practices that enable HPV transmission

HIV-infected male patients who neglect conducting sexual risk practices that enable HPV transmission

HIV-infected female patients disregarding sexual risk practices

digital-anorectal exam, liquid-based cytology, human papillomavirus testing, high-resolution anoscopy

Screening for anal squamous intraepithelial lesions

Pompeyo Viciana, MD, PhD

Karin Neukam, PharmD, PhD

César Sotomayor de la Piedra

There is increasing awareness of augmenting risk of anal cancer in people living with HIV, especially among men who have sex with men (MSM). High resolution anoscopy (HRA) represents the gold standard to detect pre-cancerous anal high-grade squamous intraepithelial lesions (HSIL), however, the procedure is time-consuming, costly and, most importantly, the learning curve is very flat. This yields a poor implementation of anal screening and, likely, to an excess of otherwise preventable anal cancer. Other screening methods include digital ano-rectal examination, anal-rectal cytology and human papillomavirus (HPV) genotyping, since infection with high-risk HPV genotypes has been identified as the main reason for the development of HSIL. To date, there is no consensus in screening strategies. Furthermore, it remains unclear whether, in whom and to which extend the currently available topic and invasive treatment options for HSIL should be applied, given that the natural history of the development of anal cancer remains poorly understood. The present cohort study aims to provide real-life data on the screening, management and follow-up of HIV-infected MSM is warranted for a better understanding of anal cancer in this setting.

The development of anal precancerous squamous intraepithelial lesions (SIL) and cancer of the anus is mainly caused by infection with high-risk human papillomavirus genotypes (HR-HPV). There is increasing awareness of marked (40 to 130-fold) risk of anal cancer in people living with HIV, especially among men who have sex with men (MSM). With the changing scenario of comorbidities in the HIV-infected population, given the success of modern antiretroviral therapy, anal cancer has emerged as a leading neoplasia across different cohorts in Western countries. In 2014, almost 30000 people were newly diagnosed with HIV in Europe, the majority of cases (77%) being men. This was largely driven by HIV transmission between MSM, which accounted for 42% of all HIV diagnoses. Hence, the incidence of anal cancer is likely to increase in the years to come.

The number of experts who support the necessity of implementing wide programs of anal cancer screening in at-risk populations is rapidly growing. Indeed, both the European Guidelines for treatment of HIV-infected adults and the Guidelines of the Spanish AIDS Society (GESIDA) currently recommend the screening of anal cancer in HIV-infected subjects at risk. It is likely that in years to come, a general recommendation to screen for anal cancer in patients with different conditions associated with immunosuppression is established, as experienced in other populations. In this context, since HIV-negative MSM and women with a history of anogenital dysplasia, as well as transplant patients, exhibit excess risk of anal cancer, these populations have been typically screened in center with ongoing anal cancer screening programs.

Digital ano-rectal examination (DARE) is mainly accepted as the first screening procedure for anal high-risk SIL (HSIL), followed by anal-rectal cytology. Both represent simple methods, however, cytology shows an alarmingly low negative predictive value and furthermore, in case of an abnormal cytology, the patient is referred to high resolution anoscopy (HRA) for biopsy of acetowhite lugol-negative lesions in the anal epithelium, suggestive of HSIL. HRA performance is similar to cervical colposcopy, but unfortunately, the training process is by far longer, hampering its implementation and availability to highly-specialized centers with large cohorts of people living with HIV under care.

Although histological HSIL was identified as precursor of anal cancer, it remains unclear if, which and in whom treatment is indicated, since not all patients progress, a considerable proportion even regresses spontaneously and recurrence rates are high. Treatments currently available include topical imiquimod or 5-fluorouracil, as well as surgical treatment. Due to the lack of data, no consensus of HSIL management has been reached.

Taken together what was mentioned above, real-life data on the screening, management and follow-up of HIV-infected MSM is warranted for a better understanding of anal cancer in this setting.

Seville Cohort of People Living With HIV at Risk for Anal Cancer

A Study to Screen for Anal Precancerous Squamous Intraepithelial Lesions and to Prevent Anal Cancer in a Real-life Setting of People Living With HIV at Different Risk: the Seville HIV Anal Cohort

The proportion of patients with cancer of the anal squamous intraepithelial cells stage 0, defined as anal high-risk squamous intraepithelial lesions (HSIL) according to the Lower Anogenital Squamous Terminology.

Incidence rates per person-years of cancer of the anal squamous intraepithelial cells stage 1-4 observed over time as defined by the National Cancer Institute of the National Institutes of Health, US Department of Health and Human Services (www.cancer.gov).

Instituto de Biomedicina de Sevilla

Hospitales Universitarios Virgen del Rocío

PharmD, PhD

Gland and Hormone Related Diseases

Adrenal Gland Neoplasms

Endocrine Gland Neoplasms

Adrenal Gland Diseases

Endocrine System Diseases

Fluorodeoxyglucose F18

Tumors are characterized by a great heterogeneity. Characterizing this intra-tumor heterogeneity is a major challenge in oncology to improve the therapeutic management and move towards personalized medicine adapted to each patient. However, intra-tumor heterogeneity remains rarely used for diagnostic purposes The discovery of an adrenal mass can occur in different circumstances. Detection of an adrenal mass can be done in a context of secreting syndrome, in the assessment of an extra-adrenal neoplasia or fortuitously when performing an imaging for another reason. The etiologies are numerous (cortical tumors, medullary tumors, metastatic lesion of a extra-adrenal neoplasia, others). The adrenal masses can be divided into two categories, depending on whether they are hyperfunctional or not. In patients without an oncological history, an adrenal mass discovered is most often a benign adenoma, but requires an endocrine assessment. In patients with known primary cancer, approximately 30% of the adrenal masses are malignant.

In all cases, the diagnostic procedure includes an imaging assessment to characterize the lesion and an endocrine assessment. CT scan performed without and after intravenous iodinated contrast agent injection is the first-line examination to assess an adrenal mass.

18F-FDG-PET may be indicated as second-line for characterizing an adrenal mass. Adrenal tumor SUVmax (Standard Uptake Value) and adrenal tumor SUVmax / liver SUVmax ratio are routinely used to determine the malignancy of a lesion. Although very useful for assessing the glucose metabolism of a given lesion, these parameters do not allow assessing the heterogeneity of tumor uptake. The texture analysis corresponds to an analysis of the spatial distribution of FDG uptake, and allows, by the calculation of many indices, an evaluation of the heterogeneity of the tumors.

The hypothesis of our study is that the texture parameters could have an additional diagnostic value to improve the performance of conventional quantitative parameters to determine the malignancy of a lesion.

The objective of this study is to investigate the diagnostic value of texture indices in a large cohort of patients presenting an adrenal lesion

18FDG PET Textural Indices in Adrenal Lesion

Diagnostic Value of 18FDG PET/CT Textural Indices in Patients With an Adrenal Lesion

For each cut off value : Positive and Negative Predictive Value (%),

University Hospital, Brest

Carcinoma, Hepatocellular

Liver Neoplasms

Neoplasms, Glandular and Epithelial

Adenocarcinoma

Liver Diseases

Hematinics

Liver Extracts

Quantitative Ultrasound

B-mode US performed according to the clinical standard of care.

B-mode Ultrasound

The composite reference standard includes MRI performed according to the clinical standard of care or histopathology when available.

Magnetic Resonance Imaging

Liver Biopsy

An Tang, MD, MSc

Casey Bourdeau Caporuscio, MSc

Worldwide, liver cancers are the third most common cause of cancer mortality. Even when liver cancer is suspected by blood tests, imaging is required to determine the location, size, and extent of disease. Medical societies therefore recommend surveillance with ultrasound every 6 months in at-risk patients. However, a key challenge to improving the survival is that ultrasound may miss half of early-stage liver cancers, thus diagnosis must rely on additional tests such as computed tomography (CT), magnetic resonance imaging (MRI), or biopsy. Hence, there is a clear need to improve the ability to detect liver cancers, especially with ultrasound. The investigator's team proposes novel ultrasound approaches to detect cancer nodules invisible on conventional ultrasound based on differences in mechanical and structural properties between liver and tumor. Improving detection is critical because liver cancer can be cured only if detected at an early stage, as shown by improvements in survival rates in patients enrolled in surveillance programs. The investigator's multi-disciplinary, national, and international team includes experts in clinical fields (hepatology, oncology, radiology, pathology), basic sciences (engineering, medical physics, machine learning, biostatistics), and patient partnership. The investirgator will apply the methodology of patient partner recruitment and collaborate with the Centre of Excellence on Partnership with Patients and the Public to select potential new collaborators. This will permit this project to be informed at every stage by patient and family perspectives, ensuring that the results of this project will be more robust, impactful, and aligned with the priorities, needs and experiences of those who live with liver cancer. The investigator submits a research proposal focused on advanced imaging techniques because imaging constitutes a foundation for surveillance, diagnosis, staging, treatment selection and assessment of treatment response in patients with liver cancer.

BACKGROUND Worldwide, liver cancers are the third most common cause of cancer mortality. In Canada, hepatocellular carcinoma (HCC) is one of the few cancers for which mortality is increasing. Early detection of HCC improves the likelihood of curative treatment and survival. Systematic HCC surveillance with ultrasound (US) is recommended by practice guidelines. However, conventional (B-mode) US suffers from low sensitivity (47%) for detecting early-stage HCC due to fatty liver, obesity, and cirrhosis. Once a suspicious nodule is detected by US, guidelines recommend contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI) to confirm cancer. Hence, there is an urgent need to improve the ability to detect and diagnose HCC early. Members of the investigator's team have developed innovative quantitative ultrasound (QUS) techniques (including shear wave viscoelastography \[SWV\] and sub-resolution cellular imaging) that have a high diagnostic potential. The investirgator hypothesizes that a combination of QUS techniques offering complementary assessment of tissue characteristics will improve our ability to detect liver nodules and diagnose HCC.

METHODOLOGY Design: Prospective, cross-sectional imaging trial comparing head-to-head B-mode US and research QUS acquired within one month to the composite reference standard. Reference standard: MRI will be used as the non-invasive ground truth for liver nodules detection and classification except when biopsy is available. Data analysis: Lesion detectability will be measured by contrast-to-noise ratio on US and QUS maps for different size thresholds (\<10 mm, 10-20 mm, \>20 mm) used in diagnostic algorithms. Diagnostic performance will be measured by receiver operating characteristic curve analysis on the training and test sets for different size thresholds. The diagnostic accuracy of US and QUS + B-mode US will be compared by using the DeLong method.

RATIONALE AND IMPACT Early detection through systematic US surveillance translates into curative therapy in a higher proportion of patients and into improvements in survival rates. This imaging trial will provide a low-cost imaging technique to identify liver cancers earlier and from a single exam. Unlike some other imaging techniques, US is widely available throughout Canada. A major impact of this work, for patients and medical institutions, will be to lower the need for liver biopsy, the risk of complications and the cost for HCC diagnosis. This trial will position Canada as a leader in HCC diagnosis as new QUS and SWV biomarkers will be tested.

Early Detection of Liver Cancer by QUS

Quantitative Ultrasound to Improve Detection and Diagnosis of Liver Cancer

Lesion detectability will be measured by contrast-to-noise ratio on US and QUS maps for different size thresholds (\<10 mm, 10-20 mm, \>20 mm) used in diagnostic algorithms

Diagnostic performance will be measured by receiver operating characteristic curve analysis on the training and test sets for different size thresholds

The diagnostic accuracy of US and QUS + B-mode US will be compared by using the DeLong method

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Breast Cancer Screening: Digital Breast Tomosynthesis Versus Digital 2D Mammography	NCT03377036	Breast Cancer S...	DBT+s2D 2D-FFDM	50 Years - 69 Years	University Hospital Muenster
Machine Learning in Myeloma Response	NCT03574454	Myeloma	Machine Learnin...	40 Years - 100 Years	Royal Marsden NHS Foundation Trust
Optical Coherence Microscopy in Dermato-oncology	NCT03731247	Skin Cancer	OCTAV Patient OCTAV Control g...	18 Years -	Centre Hospitalier Universitaire de Saint Etienne
Optical Coherence Microscopy in Dermato-oncology	NCT03079232	Skin Cancer	OCTAV Patient OCTAV Control g...	18 Years -	Centre Hospitalier Universitaire de Saint Etienne
Seville Cohort of People Living With HIV at Risk for Anal Cancer	NCT03713229	Anal Squamous I...	Screening for a...	18 Years -	Hospitales Universitarios Virgen del Rocío
Machine Learning in Myeloma Response	NCT03574454	Myeloma	Machine Learnin...	40 Years - 100 Years	Royal Marsden NHS Foundation Trust
18FDG PET Textural Indices in Adrenal Lesion	NCT03469102	Adrenal Tumor		18 Years -	University Hospital, Brest
Breast Cancer Screening: Digital Breast Tomosynthesis Versus Digital 2D Mammography	NCT03377036	Breast Cancer S...	DBT+s2D 2D-FFDM	50 Years - 69 Years	University Hospital Muenster
Early Detection of Liver Cancer by QUS	NCT06345508	Hepatocellular ... Liver Cancer	Quantitative Ul... B-mode Ultrasou... Magnetic Resona... Liver Biopsy	18 Years -	Centre hospitalier de l'Université de Montréal (CHUM)
Seville Cohort of People Living With HIV at Risk for Anal Cancer	NCT03713229	Anal Squamous I...	Screening for a...	18 Years -	Hospitales Universitarios Virgen del Rocío

Spots Global Cancer Trial Database for diagnostic performance