Neoplasms

Respiratory Tract (Lung and Bronchial) Diseases

All Conditions

Symptoms and General Pathology

Nervous System Diseases

Lung Neoplasms

Carcinoma, Non-Small-Cell Lung

Neoplasm Metastasis

Brain Diseases

Carcinoma

Respiratory Tract Neoplasms

Thoracic Neoplasms

Lung Diseases

Respiratory Tract Diseases

Carcinoma, Bronchogenic

Bronchial Neoplasms

Neoplastic Processes

Central Nervous System Diseases

Patients who developed CNS metastasis of NSCLC during the treatment.

Patients with NSCLC that does not develop CNS metastasis during the treatment.

Oscar Arrieta, MD

David Saavedra-Perez, MD

David Saavedra-Pérez, MD

Enrique Guzmán, MD

Gabriela Mercado, MSc

Elena Aréchaga, PhD

Claudia González, PhD

Digna Pachuca, MD

Non small-cell lung cancer (NSCLC) accounts 85% of all lung cancer.The development of brain metastasis diminished life expectancy to less than one year with a median survival of less than three months. In NSCLC cancer, approximately 50% of patients with locally advanced disease develop brain metastasis at some time during the natural of disease. The central nervous system constitutes the first site of recurrence in 15 to 40% of these patients. Microarrays evaluate the diagnosis, treatment and prognosis of lung cancer.There are no studies that specifically evaluate the relationship between a genetic profile of NSCLC and metastasis to the CNS, with the purpose of distinguishing a subgroup of patients that will benefit of prophylactic treatment.What is the association between a genetic profile on NSCLC and the development of CNS metastasis.Obtaining a genetic profile from the primary NSCLC tumor cells, by using microarrays, we can predict the development of CNS metastasis arise a subgroup of patients that could benefit from prophylactic cranial radiation with which their quality of life and prognosis most probably will increase.Objective:Determine the association between a genetic profile from the primary tumor cells and the development of central nervous system metastasis in patients with non small-cell lung cancer.A genetic profile from the primary tumor cells are associated with the development of central nervous system metastasis in patients with NSCLC. A clinical, prospective, analytic, open, non randomized, prognostic and observational cohort with 66 patients with NSCLC who authorize a biopsy study from February, 2008 to December, 2012, INMEGEN institute will be in charge of performing the microarrays and the computer analysis in order to obtain the different genetic profiles that will be differentially expressed related with CNS metastasis risk profiles. Patients will be followed-up by means of the external consult of lung neoplasms. The statistical analysis will be performed using tests like Student's t or Mann-Whitney's U test. A multivariate analysis of logistic regression will be performed. Global survival time will be analyzed using Kaplan-Meier's technique and the comparison between groups will be performed with log-rank test. The adjustment for potential confusors will be performed using multivariate regression analysis. For result representation, we will use tables and graphs and pertinent measures will be taken to disclose the study.

Brain metastases occur in 30-50% of lung adenocarcinoma (LAC) patients and confer a worse prognosis and quality of life. A better selection of in-risk patients through a more accurate biomarker could improve the benefit of prophylactic therapies. The aim of this prospective study was to determine a gene-expression profile of primary tumor associated with brain metastasis (BM) and to evaluate the overall survival (OS) in patients with advanced LAC.

Introduction.Lung cancer is the first cause of cancer death in the world. Eighty five percent of patients are diagnosed yearly with non-small cell lung cancer (NSCLC). Despite efforts, innovations, and progress in diagnosis and treatment of these patients, overall survival (OS) at 5 years of diagnosis is only 15%. The central nervous system (CNS) is a devastating and frequent site of metastasis development in NSCLC. The reported incidence of CNS metastasis in patients with NSCLC is 54% with an OS of \<1 year after diagnosis. Age, clinical stage, gender, and initial treatment period are some of the reported with CNS metastasis development-related factors in patients with NSCLC. We recently described that high carcino-embryonic antigen (CEA) serum levels (\>40 ng/dL) at diagnosis and adenocarcinoma histological type are independently associated with higher risk if brain metastasis development. However, due to their lack of specificity of all this reported factors, we are required to detect biomarkers to predict brain metastasis in patients with NSCLC with the objective to prevent their development.Metastasis process is complex it involves well-defined molecular-printed steps, as invasion, vascular intravasation, implantation and growing at the new specific organ. Higher motility, capacity for extracellular matrix degradation, immune system evasion, and adhesion at the new specific organ are some of the tumoral cell characteristics required to metastasize. In particular, molecular events for brain metastasis development from primary NSCLC even if no at al described, are currently well understood. Gene-expression microarray allow analyzing the expression changes of thousands of genes simultaneously, distinguishing the altered expression of neoplastic cell genes from normal tissue. Identification of biological patterns, pharmacological molecular targets, and biomarkers for prognosis and evaluation for therapeutic response, and even a more specific neoplasia classification are some of the results of gene-expression microarray studies in hematological, breast and NSCLC cancer. Determination of tobacco-smoke transcriptional changes in oncogenes and antioncogenes had been determined by the use microarray data. A gene-expression profile of 20 genes differentiates health lung tissue and lung cancer with higher specificity than histopathologic evaluation. Furthermore, gene-expression microarray studies had been developed for predicting survival and recurrence in early NSCLC stages for identification of patients who could have benefit of adjuvant therapy, with promising result.The objective of this study was to identify a gene-expression profile from primary lung adenocarcinoma related with brain metastasis, and to evaluate in a prospective manner their prognostic significance on survival in patients with advanced disease.Experimental Design These study used clinical, longitudinal, prospective, observational, and analytical cohort s with the selection of a nonprobabalistic-type sample.In a prospective manner, from January 2009 to June 2011, patients admitted to our Institute confirmed histologically confirmed stage IIIb and IV of LAC were eligible for study inclusion. Analyzed clinical variables comprised smoking history, gender, age, general condition and brain metastasis development. Primary tumor core-biopsy was performed prior to any treatment and snap-frozen. A single pathologist evaluated all tumors. Standard platinum-based chemotherapy was employed for all patients. All patients were submitted to Magnetic resonance imaging (RM) to evidence the presence or absence of brain metastasis (BM). During follow-up, we carried out Computed tomography (CT) of the brain stem for this purpose. Preliminary statistical analysis was performed utilizing the Student t, the Mann-Whitney U, the χ2, or the Fisher exact test. Once the study ends, we will conduct logistic-regression multivariate analysis. Global survival time will be analyzed with the Kaplan-Meier technique, and comparisons among groups will be performed with the log-rank test. High Risk characteristics as gender, histology, and age related with greater frequency of development of metastasis to CNS were analyzed.The study was carried out according to the principles of ClinicalTrials and accepted by the INCan Bioethical and Scientific Committees (reference numbers INCAN/CC/067/08). A collaboration agreement was signed with the INMEGEN and was approved within the Health Research Sectorial Fund (FSIS) CONACyT-México (SALUD-2009-01-115552).

Selection for obtaining tumor biopsies depended on the characteristics of the patient and of the tumor. The biopsy could be taken by means of CT guided tru-cut needle, open thoracoscopy, or bronchoscopy with optic fiber. Each tumor sample was divided into two; one sample was analyzed by the INCan Pathology Service (by a sole observer blinded to the clinical variables) for their histological clinical diagnosis and quantification of neoplastic cellularity, and the remaining half was immediately stored at -80°C until processing for RNA extraction.RNA Extraction:RNA was extracted from frozen tumor biopsies, weighted and cryofractured in liquid nitrogen. Extraction and purification of total RNA from tissue (up to 5 mg tissue) procedure was done using RNeasy Micro Kit (QIAGEN, Germany) (cat. 217084). RNA was analyzed using the Agilent 6000 Chip (Agilent Technologies, Santa Clara, C.A.). RNA quality consisted on the obtention of RNA Integrity Number (RIN) \> 8. RNA samples were chosen for microarray analysis when quality and concentration were obtained for this purpose. Microarray Expression:We are using an Affymetrix platform, which consists of an in situ synthesized oligonucleotide microarray. The GeneChip® that we are employing is the Human Gene 1.0 ST Array, which allows analyzing the expression of 28,869 different genes (each transcript is represented by 26 disperse probes along its entire length, present in the human genome, which covers 99% of the sequences present in the RefSeq data bank of the GenBank. The Two-Cycle Target Labeling protocol is followed as suggested by the manufacturer and described succinctly as follows and shown graphically in the figure at the end of this Materials and Methods section: Total RNA (100 ng/uL) is retro-transcribed using T7-Oligo(dT) Promoter primers in the synthesis reaction of the first complementary DNA chain (cDNA). After treatment with RNAase H, the second cDNA chain is synthesized, which will serve as template in the Transcription reaction in vitro (TVI). The first TVI reaction is carried out with T7 polymerase RNA and unmarked ribonucleotides for the amplification of complementary RNA (cRNA). These cRNAs are retro-transcribed in the synthesis step of the first cDNA chain of the second cycle using random primers. Subsequently, T7-Oligo(dT)-Promoter primers are employed for the synthesis of the second chain of cDNA, generating the double-helix cDNA template that contains the promoter T7 sequence. This cDNA is amplified and marked in a second TVI reaction using the T7 polymerase DNA and a mixture of ribonucleotides/biotinylated nucleotide analog. These target biotinylated cDNAs are cleaned, fragmented, and hybridized to the GeneChip® expression microarrays. After hybridization, fluorescent marking was performed with a streptavidin-phycoerythrin and biotinylated anti-streptavidin antibody amplifier system. Fluorescence is detected with a high-resolution laser scanner.Reading and Analysis of Expression Microarrays:We will utilize the Expression Console of Affymetrix software to evaluate the quality of the microarrays and will correct the background signal with standard methods \[49,50\]. For signal intensity-level normalization, we will employ "non-supervised" methods, which use the data of all of the experiment's microarrays; this will allow us to render these comparable among themselves, because what a microarray assay evaluates is gene expression by means of fluorescence intensities; thus, it is important to establish cut-off points from which we can consider under- or overexpression. Once the data are normalized, the final stage is to resume the values of all the probes that exist for each gene in the microarray in a sole value, which is the gene's expression level. For detection of differentially expressed genes, we will construct a linear model that includes all of the experiment's microarrays simultaneously; in this manner, we will be able to analyze contrasts between group A (patients with metastasis to CNS during the follow-up period) and group B (patients without metastasis to CNS during the same period). As internal control, we will prove the differential expression of some genes by RT-PCR in real time. Results will be represented in blocks of heat maps and in tables, in which the most active genes are listed along with their functional description. For risk-profile validation, we will perform leave-one-out cross validation method and will postulate a second cohort for this objective.

Gene-expression Profiles in CNS-metastatic Non-small Cell Lung Cancer

Gene-expression Profile as Predictor of Central Nervous System Metastasis Development in Non-small Cell Lung Cancer: a Prospective Study

National Institute of Cancerología

Gene expression Profiles in CNS metastasic Non small Cell Lung Cancer

Skin and Connective Tissue Diseases

Infections

Wounds and Injuries

Breast Neoplasms

COVID-19

Fractures, Bone

Pneumonia

Pneumonia, Viral

Communicable Diseases

Respiratory Tract Infections

Virus Diseases

Coronavirus Infections

Coronaviridae Infections

Nidovirales Infections

RNA Virus Infections

Breast Diseases

Skin Diseases

The experiment is conducted on 10 types of studies with AI:

1. Chest CT/ LDCT with different pathologies;
2. Abdominal CT with different pathologies;
3. Head CT with different pathologies;
4. MSS XR with different fractures
5. Spine XR with different pathologies;
6. MMG;
7. Brain MRI with different pathologies;
8. Cervical spine MRI, Lumbosacral spine MR and Thoracic spine MRI with spine pathologies
9. Knee joint MRI
10. Lesser pelvis MRI.

The experiment is conducted on 10 types of studies without AI:

1. Chest CT/ LDCT with different pathologies;
2. Abdominal CT with different pathologies;
3. Head CT with different pathologies;
4. MSS XR with different fractures
5. Spine XR with different pathologies;
6. MMG;
7. Brain MRI with different pathologies;
8. Cervical spine MRI, Lumbosacral spine MR and Thoracic spine MRI with spine pathologies
9. Knee joint MRI
10. Lesser pelvis MRI.

Kirill Arzamasov, PhD

Olga Omelyanskaya

Anton Vladzymyrskyy

It is planned to integrate various services based on computer vision technologies for analysis of the certain type of x-ray study into Moscow Unified Radiological Information Service (hereinafter referred to as URIS).

As a result of using computer vision-based services, it is expected:

1. Reducing the number of false negative and false positive diagnoses;
2. Reducing the time between conducting a study and obtaining a report by the referring physician;
3. Increasing the average number of radiology reports provided by a radiologist per shift.

Recently a growth in the number of radiology studies across multiple modalities has been observed alongside the modest increase in staffing levels. This carries higher risks of increased workload and efficiency losses. The integration of computer vision-based services into URIS will improve the radiologists' productivity and job performance.

Existing prerequisites for conducting the study:

1. Increasing the number of preventive and diagnostic radiological studies entails the growing workload for radiologists and increased risk of interpretation errors, which in turn leads to the decrease in quality of medical care.
2. When a radiologist opens a worklist of studies, in the absence of special notes, he/she writes a report in the random order, not being able to select from the list the studies that require the most attention and prompt response (studies with pathological findings), which increases the time of diagnosis.
3. The absence of the structured pre-filled template of report leads to the increase in time for preparing reports.
4. A radiologist has to spend considerable time evaluating the dynamics of pathological changes, which also increases the time to prepare a report as well as the risk of error.
5. Interpretation of preventive studies requires double reading, which is implemented inefficiently due to the staff shortage.

Study objectives:

1. Study the diagnostic accuracy of the Services in accordance with the methodological guidelines No. 43 "Clinical trials of software based on intelligent technologies (diagnostic radiology)" (recommended by the Expert Council on Science of the Moscow Healthcare Department, Protocol No. 8 of June 25, 2019).
2. Audit the studies conducted with Services application in order to determine the number of interpretation errors, and compare it with the audit result without their application (hypothesis 1).
3. Conduct timekeeping to estimate time for preparing a report and the total number of evaluated studies with and without using the Services (hypothesis 2,3).
4. Conduct a survey of radiologists who use the Services in their work, in order to determine their opinion about the implementation of innovative technologies in the diagnostic process.

METHODOLOGY

1. The Experiment is carried out by the Moscow Healthcare Department in accordance with Regulation No. 43 of January 24, 2020 "On approval of the procedure and conditions for conducting the Experiment on the use of innovative computer vision technologies for analysis of medical images and further application in Moscow healthcare system".
2. The experiment is conducted on the next types of studies:

1)Detection of CT signs consistent with COVID-19 (coronavirus) lung involvement (Chest CT); 2) Emphysema extent (Chest CT); 3) Detection of CT signs consistent with malignant neoplasm in the lungs (Chest CT); 4) Detection of LDCT signs consistent with malignant neoplasm in the lungs (Chest LDCT); 5) Detection and localization of compression vertebral fractures with a degree of vertebral body deformity of over 25% according to the Genant semi-quantitative scale, grades 2-3 (Chest CT); 6) Detection of free pleural fluid (effusion) (Chest CT); 7) Detection of enlarged intrathoracic lymph nodes (lymphadenopathy) (Chest CT); 8) Detection of bronchiectasis (Chest CT); 9) Detection of CT signs consistent with pulmonary tuberculosis (Chest CT); 10) Coronary calcium score (Chest CT/ LDCT); 11) Paricardial fat volume (Chest CT); 12) Dilation of ascending and descending thoracic aortas (Chest CT/ LDCT); 13) Dilation of the pulmonary trunk (Chest CT/ LDCT); 14) Detection of sarcoidosis (Chest CT); 15) Detection of signs consistent with the impairment of lung airness (Chest CT); 16) Detection of signs consistent with the focal lesions in the chest bones (Chest CT); 17) Detection of CT signs consistent with rib fracture (Chest CT); 18) Detection of signs of urolithiasis (Abdominal CT); 19) Detection of signs consistent with the focal lesions in the skeleton bones (Abdominal CT); 20) Detection of liver lesions (Abdominal CT); 21) Detection of CT signs consistent with gallbladder stones (Abdominal CT); 22) Detection of CT signs consistent with renal lesions (Abdominal CT); 23) Measuring the abdominal aorta dilation (Abdominal CT); 24) Detection of adrenal lesions (Abdominal/Chest CT); 25) Detection and localization of compression vertebral fractures with a degree of vertebral body deformity of over 25% according to the Genant semi-quantitative scale, grades 2-3 (Abdominal CT); 26) Automation of routine liver measurements (dimensions, liver density, choledochus diameter, portal vein diameter) (Abdominal CT); 27) Automation of routine kidney measurements (kidney size, pelvicalyceal system size) (Abdominal CT); 28) Automation of routine measurements of spleen and pancreas (size, density of the spleen and pancreas) (Abdominal CT); 29) Detection of acute ischemic stroke and its ASPECTS score (Head CT); 30) Detection of hemorrhage and its automatic volume calculation in ml or cm³ (Head CT); 31) Automation of routine measurements (ventriculometry, displacement of median structures, measurement of the craniovertebral junction) (Head CT); 32) Detection and localization of (at least 7) signs consistent with the priority disease (Chest XR); 33) Detection of signs (at least one) consistent with bone fracture (MSS XR); 34) Detection of radiologic signs (at least one) consistent with arthrosis of the joints (MSS XR); 35) Detection of radiological signs (at least one) consistent with deforming arthrosis of the hip (MSS XR); 36) Detection of radiological signs (at least one) consistent with the fracture of the shoulder joint bones (MSS XR); 37) Detection of radiological signs (at least one) consistent with the fracture of the hip joint bones (MSS XR) 38) Detection of radiological signs (at least one) consistent with the fracture of the ankle joint bones (MSS XR).

39) Detection of reduced pneumatization / opacity of the paranasal sinuses (Head XR) 40) Detection of signs (at least one) consistent with transverse flat foot (MSS XR) 41) Detection of signs (at least one) consistent with the longitudinal flat foot in the lateral plane (MSS XR); 42) Detection of the signs of osteoporosis: detection and localization of compression vertebral fractures with a degree of height loss of over 25% as well as the radio density measurements of vertebral bodies (Spine XR); 43) Detection of signs consistent with osteochondrosis in the frontal and/or sagittal plane (Spine XR); 44) Detection of signs consistent with scoliosis in the frontal plane (Spine XR); 45) Detection of signs consistent with spondylolisthesis in the sagittal plane (Spine XR); 46) Detection and localization of findings consistent with breast cancer (MMG); 47) Detection of multiple sclerosis (Brain MRI); 48) Detection and localization of intracranial neoplasms (extracerebral, intracerebral) (Brain MRI); 49) Automation of routine measurements (ventriculometry, displacement of median structures, measurement of the craniovertebral junction, changes in white matter, intracranial measurements) (Brain MRI); 50) Detection of signs consistent with the focal lesions in the cervical spinal cord (Cervical spine MRI); 51) Detection and localization of MRI signs (at least one) consistent with degenerative changes in the cervical discs on sagittal and axial T2-WI (Cervical spine MRI); 52) Detection and localization of MRI signs (at least one) consistent with degenerative changes in the thoracic discs on sagittal and axial T2-WI (Thoracic spine MRI); 53) Detection of signs consistent with the focal lesions in the thoracic spinal cord (Thoracic spine MRI); 54) Detection and localization of MRI signs (at least one) consistent with degenerative changes in the lumbosacral discs on sagittal and axial T2-WI (Lumbosacral spine MRI); 55) Detection of signs consistent with the focal lesions in the lumbosacral spinal cord (Lumbosacral spine MRI); 56) Detecting signs consistent with the areas of cartilage breakdown (chondromalacia) along the articular surfaces of the knee and the patellofemoral joint (Knee joint MRI); 57) Automated routine measurements of the prostate gland (dimensions) (Lesser pelvis MRI); 58) Automated routine measurements of the uterus (corpus and cervix: position, dimensions, displacements) (Lesser pelvis MRI).

3. For each Service during the Experiment, a certain number of studies is provided for processing based on their type:

1. CT/LDCT - 30 250 studies;
2. XR - 55 000 studies;
3. MMG - 48 500 studies;
4. MRI - 22 500 studies.

4. A methodology for including services in the Experiment has been developed. For each Service, the participation process in the Experiment consists of the following stages:

1. selection;
2. the preparatory stage;
3. the main stage;
4. the final stage.

During the Experiment, a radiologist will routinely be able to:

* work on a sorted list of patients (triage);
* work with images processed by the Service;
* work with a pre-filled template of the radiological report on each study;
* evaluate the work of the Service according to the developed questionnaire. During the Experiment, a patient will receive the individual plan of the follow-up support. It includes preventive examinations or observation as well as treatment by a specialist.

Systematization and final analysis of the Experiment results is carried out within three months from the completion date of the last Service participation in the Experiment.

Based on the results of the Experiment, recommendations can be prepared on the possibility to register certain services as a medical device (software).

Experiment on the Use of Innovative Computer Vision Technologies for Analysis of Medical Images in the Moscow Healthcare System

Change of at least 30% in the number of errors in interpretation of the studies with using computer vision-based services compared to the number of errors in interpretation without their application.

Change of at least 30% of the time from the study completion to report finalization by a radiologist.

Change of at least 30% in the number of radiology reports provided by a radiologist per shift.

Change of at least 30% in computer vision-based services errors as per integrated feedback form for radiologists in the PACS.

Types of errors:

1. Technological defect (absent AI-generated series, partially generated AI series, DICOM SR and images mismatch, multiple conflicting results)
2. Major discrepancy (findings outside the region of interest, irrelevant findings)
3. Inaccurate diagnosis
4. Inaccurate lesion localisation
5. Inaccurate lesion classification
6. Other (free-text field)

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization	Link
Gene-expression Profiles in CNS-metastatic Non-small Cell Lung Cancer	NCT00862173	Brain Disease Metastasis Lung Cancer		18 Years -	National Institute of Cancerología
Experiment on the Use of Innovative Computer Vision Technologies for Analysis of Medical Images in the Moscow Healthcare System	NCT04489992	AI (Artificial ... Mammary Cancer Lung Cancer X-Rays; Lesion COVID-19 Chest--Diseases Abdomen Disease Brain Disease Fractures, Bone		18 Years -	Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

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