Neoplasms

Respiratory Tract (Lung and Bronchial) Diseases

All Conditions

Carcinoma, Non-Small-Cell Lung

Lung Neoplasms

Carcinoma

Respiratory Tract Neoplasms

Thoracic Neoplasms

Lung Diseases

Respiratory Tract Diseases

Carcinoma, Bronchogenic

Bronchial Neoplasms

All Drugs and Chemicals

Immunomodulating Agents

Treatment naive patients with metastatic NSCLC (stage four) with life expectancy of more than three months.

Treatment naive patients diagnosed with NSCLC.

Participants in the aRECIST group will undergo a blood draw for liquid biopsy analysis at baseline and follow up visits

Liquid biopsy

Lise Barbeaux

Mariaelena Occhipinti, MD PhD

Jan P Van Meerbeeck, MD

SALMON is a prospective, multi-center, multi-country, biomarker validation study that synergizes an extensive non-interventional biomarker discovery study on diagnostic images and tissue biopsies of non-small cell lung cancer NSCLC (rATLAS) with a smaller biomarker minimally interventional study on patients with metastases who undergo liquid biopsy and imaging follow-up for 2 years (aRECIST). A total of 1120 patients will be screened to get 1000 participants enrolled in rATLAS, and a subset of 250 participants will be screened to then recruit 150 participants also for aRECIST. The study will end after one visit for participants in rATLAS while there is a 2-years follow-up period for participants in aRECIST. Participants will not receive any treatment specific for this study, but might receive standard of care therapy or investigational products in the framework of another clinical study following the baseline visit.

The objectives of optimizing AI based tools for the assessment of EGFR status (rATLAS) and automated Response Evaluation Criteria in Solid Tumours 1.1 (RECIST 1.1) (aRECIST) will be achieved using a trial design that combines a biomarker discovery study design (cross-sectional for rATLAS) with a reader study design (follow-up study in aRECIST). Medical treatments in the aRECIST cohort are not dictated by study protocol, rather determined by the clinicians in line with standard clinical practice.

Lung cancer is the leading cause of cancer-related death worldwide, accounting for an estimated 2.1 million deaths in 2018. About 80%-85% of lung cancers are NSCLC. The main subtypes of NSCLC are adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. These subtypes, which start from different types of lung cells, are grouped together as NSCLC. For these reasons, the choice of NSCLC for this trial is the most relevant, as there is a clear societal burden / clinical need that will be addressed in SALMON.

A pressing need exists to uncover the genetic and molecular composition of tumors, as this information would accelerate the development of more effective cancer therapies. Since tumors differ in their biological makeup, treatments can now often be tailored towards individual patients, in a strategy termed personalized medicine. NSCLC perfectly illustrates this paradigm, with treatments either targeted at well-defined oncogenic pathways (epidermal growth factor receptor (EGFR) mutations, Anaplastic lymphoma kinase (ALK) \& receptor tyrosine kinase 1 (ROS1) gene rearrangements, B-Rapidly Accelerated Fibrosarcoma gene (BRAF) mutations), and for so-called "wild-type" NSCLC, immunotherapy (with or without chemotherapy). The recent development of different types of immunotherapy has led to promising advances in the treatment of patients with NSCLC in advanced or metastatic disease.

Medical imaging plays a pivotal role in the assessment of tumors, including lung cancer, as it provides non-invasively the features to identify, characterize and stage the local tumor and its overall metastatic burden. The information provided by imaging can be still improved with a thorough post-processing analysis of the images, with "radiomics" being the most sophisticated tool for quantitative imaging. Through radiomics the imaging data obtained can be linked to specific biological properties of the tumor, acquired either from tissue or liquid biopsies. However, the knowledge on this is fragmented to date.

The first objective of this trial is to generate a radiomics atlas (rATLAS) that provides a broader link between biological descriptors and medical imaging features in NSCLC, to eventually identify oncologic pathways through medical images and quantify the overall burden of specific markers for targeted therapies. In particular, imaging biomarkers that can predict immunosensitivity and more accurately predict the prognosis than the existing ones will be assessed on the medical images and compared to tissue and liquid biopsies.

Apart from NSCLC diagnosis and staging, imaging plays a key role also in the evaluation of patient's follow-up, representing the gold standard for the assessment of response to therapy. However, the evaluation of medical images is amenable to radiologist's interpretation and many attempts have been made to face this issue in the last decades. The RECIST is a one-dimensional measure (shortest/longest diameter in the plane of measurement) created in 2000, updated in 2009 (RECIST 1.1), with a branch for immunotherapy finalized in 2017 (iRECIST). RECIST, in all its different declination, is used to assess whether a tumor in cancer patients is progressing, regressing or did not change before and after some event, such as therapy. RECIST criteria are recognized both in the EU and in US. Although representing an international attempt to overcome the subjective evaluation of tumor response to cancer treatment, biases of measurements remain as it relies on human assessments. In 1976 Moertel and Hanley acknowledged that "the culmination of most experimental therapeutic trials for solid tumors occurs when a \[physician\] places a ruler or caliper over a lump and attempts to estimate its size," and with this measurement comes the inevitable component of human error. In 2021 this issue is still present in clinical practice, but in the last years Artificial Intelligence (AI) has demonstrated remarkable progress in image-recognition tasks in research.

Thus, the investigators propose to eliminate the shortcomings of measurement biases with the second objective of our trial that is focused on the validation of a new automated RECIST (aRECIST) workflow for the assessment of therapy response.

A signed and dated informed consent form will be obtained from each patient before any screening procedures are conducted. The participants will be prospectively enrolled to rATLAS only, to both rATLAS and aRECIST, or considered as screen failure. If enrolled then a unique study-specific subject number shall be assigned, Informed consent form will be signed and the following evaluations will be included:

The following data will be obtained for each participant from medical files and records:

* Demographic data (Age, Gender, Race, country);
* Anthropometric data (Weight in kg, Height in cm, body mass index (BMI) in kg/m\^2);
* Medical history (comorbidities, oncology-related history, Chronic obstructive pulmonary disease (COPD), Interstitial lung disease (ILD), Pulmonary vascular disease, history related to NSCLC);
* Substance use (cigarettes/e-cigarettes/alcohol), duration and dose/frequency \[smoking required, others optional\];
* Concomitant treatment only linked to immunotherapy, chemotherapy, radiotherapy, and targeted therapy (subtype, drug name, line of treatment (first/second/third line), reason for discontinuation (completed regimen/completed response/progression disease/toxicity/other);
* Eastern Cooperative Oncology Group (ECOG) performance status (0 to 5);
* Histological diagnosis (adenocarcinoma, squamous cell carcinoma, large cell carcinoma, adenosquamous cell carcinoma, other);
* Laboratoristic data: Hematology (hemoglobin, hematocrit, leukocytes, neutrophils, eosinophils, basophils, lymphocytes, monocytes, platelets) and Chemistry (sodium, bicarbonate, chloride, calcium, albumin, magnesium, phosphate, blood urea nitrogen, protein, urea, potassium, creatinine, estimated glomerular filtration rate, bilirubin, direct bilirubin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase, lactate dehydrogenase (LDH), carcinoembryonic antigen (CEA), squamous cell carcinoma (SCC)).

From clinical examination and imaging (and multidisciplinary discussion report if available) the following data will be recorded per participant: Clinical Tumor Staging (cTNM) upon initial diagnosis.

From Radiology department the following data will be recorded per participant: The staging computed tomography (CT) scan data already performed at the site per standard of care (SoC) will be pseudo-anonymized and sent to the central reader, along with Positron Emission Tomography (PET)/CT and brain magnetic resonance imaging (MRI) scans obtained per patient staging, when available.

Then, depending on group (aRECIST or rATLAS), the following steps will also be considered:

rATLAS: Diagnostic samples of tissue biopsy (ideally 1 block or 25 slides - minimum required: 10 slides) performed at the participating site per standard of care will be shipped to the central laboratory for testing on oncogenic biomarkers. After this baseline visit all participants in rATLAS may start their treatment as per SoC. rATLAS study ends after this baseline and enrolment visit.

aRECIST: A liquid biopsy will be obtained (blood sample, 1 tube of 10 ml) and shipped within 2 days to the central laboratory for oncogenic biomarker testing. In order not to influence therapeutic decision making, biomarker results will be shared with the sites only after the decision concerning NSCLC therapy has been made. Diagnostic samples of tissue biopsy which have been obtained for ATLAS (ideally 1 block or 25 slides - minimum required: 10 slides) performed at the participating site per standard of care will be shipped to the central laboratory for oncogenetic biomarker testing. In those cases where standard tissue biopsy is not obtained, fine needle biopsy and cells obtained per SoC by fine needle aspiration could be analyzed and send to the central laboratory following specific procedures. Enough material should be available to be analyzed, otherwise the genetic testing would not be possible.

After baseline all participants of the aRECIST group will undergo standard chemotherapy and/or immunotherapy and will be followed up for a maximum of 2 years to investigate a possible treatment response (manual local \& central and automated RECIST) by imaging parameters and liquid biopsy. These participants will need to return to the clinic for liquid biopsies (2 tubes of 10 mL) at 3, 6, 12 and 24 months within 5 days (before or after) of their imaging follow-up as per SoC. It is the site staff that will be responsible for providing a copy of all relevant post-treatment CT-scans (and PET/CT scan if available) as taken per SoC together with local RECIST scoring, NSCLC treatment(s) information (class, dose, duration) and NSCLC-related events. The data will be collected at time-points as per standard of care (at 3, 6, 12, and 24 months approximately). The participant's participation to aRECIST will end 2 years after baseline or earlier in case of death or any event that in the opinion of the investigator requires an early termination.

In particular, the site staff will be responsible to provide a liquid biopsy (2 tubes of 10ml each) and to share the following data at 3 (visit1), 6 (visit 2), 12 (visit 3), and 24 (visit 4) months):

* A copy of all relevant post-treatment CT scans - and PET/CT scans if available - as taken per SoC
* Detailed description of CT acquisition for each participant, including contrast media injection characteristics
* Local reading of treatment response by using RECIST 1.1 criteria (manual local reading), along with the time needed for evaluating each participant,
* A liquid biopsy (2 tubes of 10 ml each) within 5 days of the CT scan performed for participant follow up
* NSCLC treatment(s) information (class, dose, duration, specific timeframes)
* NSCLC-related events.

Any rATLAS participant are considered as having completed the study after performing the initial baseline visit (Day 0), except for those also participating to aRECIST. Participants of aRECIST are considered as having completed the study after the 2-year follow-up period, or earlier, in case of death or any event that in the opinion of the investigator requires an early termination.

Blood sample for liquid biopsy analysis (DNA and RNA analysis, immunohistochemistry) Tumor biopsy sample (DNA and RNA analysis, immunohistochemistry)

Artificial Intelligence for Help Non-Small Cell Lung Cancer: Measure Cancer Biology and Treatment Response Via Imaging

Artificial Intelligence to Help Non-Small Cell Lung Cancer Patients: Measure Lung Cancer Biology and Treatment Response Via Imaging

Equivalence will be assessed by comparing manual RECIST target response (central readings) with the target response as generated by the automated Radiomics aRECIST workflow. Categorical similarity measures between central panel RECIST and aRECIST will be computed through Cohen's kappa coefficient. aRECIST is considered successful if kappa is at least 0.7 (lower bound), in the full dataset of 150 patients.

A radiomics-based EGFR mutation prediction model will be trained and tested. The EGFR mutation prediction model is considered successful if its AUC of ROC is ≥ 0.65 in the independent test set of 200 patients.

Statistical testing on the performance difference between models predicting survival at 24 months will be performed to compare the prognostic value of aRECIST to that of RECIST. Categorical similarity measures between central panel RECIST and local RECIST will be computed through Cohen's kappa coefficient. Inter-reader agreement within the central panel will be computed trough the kappa coefficient.

This is assessed by comparing biomarker results from solid biopsies with CT-scan imaging features collected at the baseline visit. A customized radiomics approach to identify statistically significant differences in radiomics features between numerous genomic/biological statuses will be used to map the rATLAS.

University Hospital, Antwerp

OncoRadiomics

Skin and Connective Tissue Diseases

Skin Diseases

Skin Neoplasms

A Real-time Augmented Reality Device with Artificial Intelligence Integration, acquisition of patient skin lesion images as data

Patients are diagnosed and treated with the assistance of artificial intelligence, augmented reality and new optical imaging technology, which is different from traditional model.

A Real-time Augmented Reality Device with Artificial Intelligence Integration

Kai Huang

In response to clinical needs, infrared multi-spectral images are combined with traditional clinical images and other multi-modal data to build a more efficient intelligent auxiliary diagnosis system and intelligent equipment for skin health and diseases, including skin lesions automatically segmentation on skin diseases images, automatically design surgical margin and planning for skin tumor surgery.

Database: Relying on the preliminary foundation, build the first standardized infrared multispectral image database of skin diseases, and further integrate other medical images and medical history texts to iterate into a large multimodal skin disease database.

Model: Design a deep learning network based on multi-scale and multi-level. The collaborative attention learning network realizes the collaborative representation of multi-modal data at the feature level, builds a multi-modal skin disease auxiliary diagnosis model, and realizes breakthroughs in algorithms. Develop the segmentation network of skin lesions and model for surgery planning, including surgical margin design and navigation of intraoperative sampling.

System: Propose an artificial intelligence system combined with the real-time augmented reality to assist dignosis and surgery for skin diseases.

Equipment: Based on the self-developed high-performance system, construct and assemble infrared multi-spectral skin disease auxiliary diagnosis equipment and multifunctional device for skin tumors surgery.

A Multi-centric Clinical Trial in China for Skin Diseases Intelligent Diagnosis and Treatment System

Based on Artificial Intelligence, Augmented Reality and New Optical Imaging Technology, the Research and Development of a Skin Diseases Intelligent Diagnosis and Treatment System: A Multi-centric Clinical Trial in China

Compare the proposed system's perfomance with the doctors in the terms of the diganosis and lesion segmentation. After using the system and the device, doctors evaluated its performance on the Skin Lesion Boundary Description, Margin Design, Sampling Navigation, Projection Effect, Security, Time-consuming and Convenience. 0-10 points for each indicator is scored independently by 4 doctors.

Xiangya Hospital of Central South University

Group 1: Patients with newly diagnosed gynecologic cancers (including cervical, endometrial, and ovarian cancers).

Group 2: Patients with suspicious recurrent gynecologic cancers (including cervical, endometrial, and ovarian cancers).

Group 3: Patients with ovarian cancer who had debulking surgery and are going to receive adjuvant chemotherapy.

Medical Imaging

Tiffany Ting-Fang Shih, MD

Shih Tiffany Ting-Fang, MD

In this prospective cohort study, patients with newly diagnosed gynecologic cancers (including cervical, endometrial, and ovarian cancers) and patients with suspicious recurrent gynecologic cancers will be enrolled. The study period is two years and the estimated patient number is about 220. Patients will undergo image study before and after treatment. The diagnostic accuracy of MR-PET in cancer staging and the potential image biomarkers to monitor treatment response and predict treatment outcome will be investigated.

Application of Integrated MR-PET in Patients With Gynecologic Cancers

National Taiwan University Hospital

Symptoms and General Pathology

Digestive System Diseases

Neoplasm Metastasis

Colorectal Neoplasms

Intestinal Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Colonic Diseases

Intestinal Diseases

Rectal Diseases

Neoplastic Processes

Antineoplastic Agents

Hematinics

Bevacizumab

Antibodies

Antibodies, Monoclonal

Cetuximab

Angiogenesis Inhibitors

Immunoglobulins

Liver Extracts

Antineoplastic Agents, Immunological

Growth Inhibitors

cetuximab

bevacizumab

Jean-Alain Chayvialle, MD

Primary Objective: This trial is elaborating a model for rapidly predicting (day 21) the response to monoclonal antibodies anti-EGFR and anti-VEGF (cetuximab and bevacizumab) based on biological markers and/or functional imaging. The response to treatment is evaluated by the conventional method after 2 months (Response Evaluation Criteria in Solid Tumors \[RECIST\] criteria).

Secondary Objectives:

1. This trial is also analyzing the correlation between the magnitude of response to treatment at 2 months (stabilization or objective response, RECIST criteria) and that of response observed after 6 months of treatment.
2. The organisational objective is to develop a tumour bank of metastatic colorectal cancer.

Population: The population includes 252 male and female patients with metastatic colorectal cancer justifying the use of cetuximab or bevacizumab, with no heart disease.

Techniques: Computed tomography (CT scan), functional imaging (ultrasound with SonoVue); molecular imaging (positron emission tomography \[PET\] with fluorodeoxyglucose F18 \[18-FDG\]); and biology and pathology on microbiopsy of liver metastasis are used.

Outcome Criteria: The primary outcome is response to treatment with monoclonal antibodies according to RECIST criteria at two months.

Studied Factors:

Radiology:

1. CT scan: RECIST criteria (gold standard);
2. Ultrasound with SonoVue injection: 1 representative target (delay of contrast appearance, peak of rising, curve of increase and decrease of the signal, area under the curve, time of average transit).

Nuclear Medicine: PET scan and 18-FDG (standard uptake values \[SUV\])

Molecular Characterization of Tumors: p53 status; microsatellite instability (MSI) status; expression of oncogenes; EGFR status; VEGF status; determination of FcgammaRIIIA polymorphisms

Statistics:

1. Descriptive analyses;
2. Analysis of the appropriate threshold to measure: response to treatment by an ultrasound with SonoVue and by PET scan; correlation between response predicted by the ultrasound with SonoVue and the PET; conventional morphological CT at 2 months
3. Analysis of prognostic factors:

   1. Evaluation of the role of each prognostic factor (pathology and imaging) on response to treatment;
   2. Multivariate analysis of prognostic factors;
   3. Analysis of the prognostic power of early response at 2 months on the response observed after 6 months of treatment.

Elaboration of a Model for Predicting Efficacy of Monoclonal Antibodies (Cetuximab and Bevacizumab) in Patients With Colorectal Cancer and Liver Metastases

Hospices Civils de Lyon

Jean-Alain CHAYVIALLE, Pr

Urinary Tract, Sexual Organs, and Pregnancy Conditions

Rare Diseases

Carcinoma, Renal Cell

Cysts

Kidney Diseases

Kidney Diseases, Cystic

Pathological Conditions, Anatomical

Urologic Diseases

Urogenital Diseases

Female Urogenital Diseases

Pregnancy Complications

Female Urogenital Diseases and Pregnancy Complications

Male Urogenital Diseases

Renal Cell Carcinoma

Clear Cell Renal Cell Carcinoma

The high-throughput extraction of large amounts of quantitative image features from radiographic medical images

Radiomics

More than 200,000 new cases of renal cancer are diagnosed in the world each year, with more than 63,000 new cases in Europe alone. Of those, renal cell carcinoma (RCC) is the most common type in adults, making up more than 90% of the cases. Deciding on the benign or malignant nature of some RCC on the basis of medical images (CT, MRI, US) is an issue, which often leads to unnecessary surgery, morbidity and costs.

A categorization for renal cysts was introduced in the late 1980s known as the Bosniak classification. The Bosniak classification system classifies them into groups that are benign (I and II) and those that need surgical resection (III and IV), based on specific imaging features. However, defining the malignancy of category III lesions still remains a challenge. Though Bosniak classification for renal cysts is used worldwide and underwent a number of modifications, Bosniak III cysts still have almost a 1:1 chance of being malignant. So the problem is that approximately half of the Bosniak category III cystic lesions prove to be benign after surgery.

The proposed project aims to develop a quantitative image analysis (QIA) based multifactorial decision support system (mDSS) capable of classifying renal cysts with high accuracy into benign or malignant status, thus reducing the amount of unnecessary surgeries performed. Using standard-of-care CT images and clinical parameters, the customized DSS will then guide experts in planning a safe and effective diagnostic and treatment strategy for all RCC patients.

The Potential of Radiomics to Differentiate Between Malignant and Benign Bosniak 3 Renal Cysts.

Machine learning algorithm that can differentiate between malignant and beingn bosniak 3 renal cysts.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Artificial Intelligence for Help Non-Small Cell Lung Cancer: Measure Cancer Biology and Treatment Response Via Imaging	NCT05254132	Non Small Cell ...	Liquid biopsy	18 Years -	OncoRadiomics
A Multi-centric Clinical Trial in China for Skin Diseases Intelligent Diagnosis and Treatment System	NCT05463523	Skin Diseases Artificial Inte... Augmented Reali... Medical Imaging	A Real-time Aug...	-	Xiangya Hospital of Central South University
Application of Integrated MR-PET in Patients With Gynecologic Cancers	NCT02292615	MR-PET Imaging	Medical Imaging	20 Years - 90 Years	National Taiwan University Hospital
Elaboration of a Model for Predicting Efficacy of Monoclonal Antibodies (Cetuximab and Bevacizumab) in Patients With Colorectal Cancer and Liver Metastases	NCT00327093	Colorectal Canc... Neoplasm Metast...	cetuximab bevacizumab	18 Years - 75 Years	Hospices Civils de Lyon
A Multi-centric Clinical Trial in China for Skin Diseases Intelligent Diagnosis and Treatment System	NCT05463523	Skin Diseases Artificial Inte... Augmented Reali... Medical Imaging	A Real-time Aug...	-	Xiangya Hospital of Central South University
The Potential of Radiomics to Differentiate Between Malignant and Benign Bosniak 3 Renal Cysts.	NCT03552497	Renal Cyst Comp...	Radiomics	-	Maastricht University

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