Neoplasms

All Conditions

Respiratory Tract (Lung and Bronchial) Diseases

Carcinoma

Carcinoma, Non-Small-Cell Lung

Lung Neoplasms

Carcinoma, Bronchogenic

Bronchial Neoplasms

Respiratory Tract Neoplasms

Thoracic Neoplasms

Lung Diseases

Respiratory Tract Diseases

Forceps biopsy

Endobronchial biopsy with the cryobiopsy probe

Cryobiopsy

Maik Haentschel, MD

Juergen Hetzel, MD

Study design Prospective multicentre explorative randomized single blinded study to evaluate accuracy of molecular genetic characterisation of NSCLC. Patients with suspected lung cancer are randomized in a 1:1-setting for bronchoscopic tumor tissue either by forceps or by cryobiopsy. Apart from the bronchoscopic techniques liquid biopsy of peripheral blood and if feasible transbronchial needle aspiration with or without endobronchial ultrasound guidance are performed for in all patients.

Objectives

Primary Objective:

assessment of differences in detection of molecular genetic alterations in NSCLC between bronchoscopic forceps biopsy and bronchoscopic cryobiopsy

Secondary Objective:

assessment of differences in detection of molecular genetic alterations in NSCLC between

* liquid biopsy, solid tumor tissue by bronchoscopic techniques, cytologic material by TBNA
* combination of methods (tissue biopsy, TBNA and liquid biopsy) and single techniques
* naïve and processed tumor tissue specimen (eg. microdissection)

To assess differences in side effects e.g. periinterventional bleeding

Explorative Objective:

To explore tumor mutational burden with regard to

* solid tumor tissue by bronchoscopic forceps biopsy by bronchoscopic cryobiopsy
* cytologic material by (EBUS-guided) TBNA
* liquid biopsy

Target subject population Patients with suspected lung cancer or proven NSCLC and visible tumor suspicious lesion(s) requiring tissue diagnosis form the study population of this trial.

Influence of Biopsy Technique on Moleculargenetic Tumor Characterisation in NSCLC

A Prospective, Randomized, Single Blinded Multicentre Trial to Evaluate Molecular Genetic Characterisation of Primary Diagnosed or Relapsed Non Small Cell Lung Cancer by Single or Combination of Diagnostic Procedures

to explore tumor mutational burden with regard to

* Solid tumor tissue by bronchoscopic forceps biopsy by bronchoscopic cryobiopsy
* Cytologic material by (EBUS-guided) TBNA
* Liquid biopsy

assessment of differences in detection of molecular genetic alterations in NSCLC between bronchoscopic forceps biopsy and bronchoscopic cryobiopsy

assessment of differences in detection rate of molecular genetic alterations in NSCLC between

* different bronchoscopic (forceps/ cryobiopsy) specimens (No 1 to No 4)
* liquid biopsy, solid tumor tissue by bronchoscopic techniques, cytologic material by TBNA
* combination of methods (tissue biopsy, TBNA and liquid biopsy) and single techniques
* naïve and processed tumor tissue specimen (eg. microdissection) To assess differences in side effects e.g. periinterventional bleeding

assessment of differences in the quantity of total mutational burden between

* different bronchoscopic (forceps/ cryobiopsy) specimens (No 1 to No 4)
* liquid biopsy, solid tumor tissue by bronchoscopic techniques, cytologic material by TBNA
* combination of methods (tissue biopsy, TBNA and liquid biopsy) and single techniques
* naïve and processed tumor tissue specimen (eg. microdissection) To assess differences in side effects e.g. periinterventional bleeding

AstraZeneca

University Hospital Tuebingen

Infections

Symptoms and General Pathology

COVID-19

Communicable Diseases

Tuberculosis

Tuberculosis, Pulmonary

Mycobacterium Infections

Bacterial Infections

Gram-Positive Bacterial Infections

Mycoses

Bacterial Infections and Mycoses

Disease Attributes

Respiratory Tract Infections

Respiratory System Agents

All Drugs and Chemicals

Participants with lung cancer/pulmonary nodules

wei geng, Phd

Yang Jin, MD

Yang Jin, Professor

To improve accurate diagnosis and treatment of common malignant tumors and major infectious diseases in the respiratory system, we aim to establish a large medical database that includes standardized and structured clinical diagnosis and treatment information such as electronic medical records, image features, pathological features, and multi-omics information, and to develop a multi-modal data fusion-based technology system for individualized intelligent pathological diagnosis and therapeutic effect prediction using artificial intelligence technology.

The main aims are as follows:

1. To establish a medical big data platform for multi-modal information fusion of common tumors and major infectious diseases (lung cancer/pulmonary nodules, tuberculosis, and COVID-19) based on the existing pathological image features and clinical multi-omics information database: The medical big data platform supports the acquisition of the patient's clinical electronic medical records (including routine clinical detection), full view digital section of pathological image data, medical imaging (CT, MRI, ultrasound, nuclear medicine, etc.), multiple omics data (genome, transcriptome, and metabolome, proteomics) omics data, etiology, pathology, and associated graphic data reports and multimodal medical treatment data. We aim to realize the storage, sharing, fusion computing, privacy protection, and security supervision of multi-modal and cross-scale biomedical big data. Our work will open up key business processes and links across regions, across hospitals, between different terminals, between hospitals and doctors, and between departments, so as to promote continuous data accumulation and knowledge precipitation in hospitals and promote medical collaboration.
2. To create a multimodal information fusion database with pathologic features, imaging features, multi-omics (pathologic, genomic, transcriptome, metabolome, proteomics, etc.), and clinical information of patients at different stages of lung cancer/pulmonary nodules, tuberculosis, and COVID-19. The database scale includes multimodal data of at least 600 lung cancer/pulmonary nodules, 200 tuberculosis, and 200 COVID-19 patients. Moreover, there will be more than 10 biomarkers significantly related to the diagnosis and treatment of patients with lung cancer/pulmonary nodules, tuberculosis and COVID-19 were excavated through association analysis, providing parameters for artificial intelligence model construction.
3. We will make use of artificial intelligence technology to create the multi-modal medical big data cross-analysis technology and the above disease individualized accurate diagnosis and curative effect prediction models. In order to solve the three key problems of multi-modal data fusion mining, such as unbalanced, small sample size, and poor interpretability, we will establish an ARTIFICIAL intelligence recognition algorithm for image images and pathological images, and use image processing and deep learning technologies to mine multi-level depth visual features of image data and pathological data. In addition, we will use bioinformatics analysis algorithms to conduct molecular network mining and functional analysis of molecular markers at the level of multiple omics technologies (pathologic, genomic, transcriptome, metabolome, proteome, etc.).

Development of an Artificial Intelligence System for Intelligent Pathological Diagnosis and Therapeutic Effect Prediction Based on Multimodal Data Fusion of Common Tumors and Major Infectious Diseases in the Respiratory System Using Deep Learning Technology.

Research and Development of an Artificial Intelligence Technology System for Digital Pathological Diagnosis and Therapeutic Effect Prediction Based on Multimodal Data Fusion of Common Tumors and Major Infectious Diseases in the Respiratory System Using Deep Learning Technology.

The outcome of clinical diagnosis of patients with lung cancer/pulmonary nodular (Benign/Malignant nodule).

① Benign nodule

② Malignant neoplasm/nodule: squamous cell carcinoma, adenocarcinoma, small cell carcinoma, and large cell carcinoma.

The outcome of clinical diagnosis of patients with pulmonary tuberculosis (Positive/Negative).

The outcome of clinical diagnosis of patients with COVID-19 (Positive/Negative).

The treatment response of anti-cancer therapy at first evaluation in patients with lung cancer/pulmonary nodules follows The Response Evaluation Criteria In Solid Tumors (RECIST version 1.1) from the World Health Organization (WHO). The evaluation index is as follows.

CR (complete response): Disappearance of all target lesions and reduction in the short axis measurement of all pathologic lymph nodes to ≤10 mm.

PR (partial response): 30% decrease in the sum of the longest diameter of the target lesions compared with baseline.

PD (progressive disease):≥20% increase of at least 5 mm in the sum of the longest diameter of the target lesions compared with the smallest sum of the longest diameter recorded OR The appearance of new lesions, including those detected by FDG-PET (fludeoxyglucose positron emission tomography).

SD (stable disease): Neither PR nor PD.

Treatment response of anti-inflammation and antiviral therapy at first evaluation in patients with COVID-19 (effective/ineffective treatment).

effective treatment： Improved total time to recovery, resolution of fever, cough remission, and pneumonia severity.

ineffective treatment： The above conditions have not improved or patients go die.

Treatment cure： patients with bacteriologically confirmed TB at the beginning of treatment who were smear- or culture-negative in the last month of treatment and on at least one previous occasion.

Treatment completer: patients who completed treatment without evidence of failure but with no record to show that sputum smear or culture results in the last month of treatment and on at least one previous occasion were negative.

Treatment success: The sum of cured and treatment completed.

Treatment failure: patients whose sputum smear or culture is positive at month 5 or later during treatment.

Treatment relapse: Patients who were declared cured or treatment completed at the end of their most recent course of TB treatment, and are now diagnosed with a recurrent episode of TB. This can be either a true relapse or a new episode of TB caused by reinfection.

Patient died.

The time interval between the date of treatment initiation and disease progression (Months) of patients with lung cancer/pulmonary nodules.

The time interval between the date of diagnosis and death (Months) of patients with lung cancer/pulmonary nodules.

Whole-genome sequencing of blood samples before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Whole-genome sequencing is mainly used to find single nucleotide polymorphisms (SNPs), copy number variations, and insertions/deletions.

Whole-genome sequencing of tissue samples after surgery in patients with lung cancer/pulmonary nodular and tuberculosis. Whole-genome sequencing is mainly used to find single nucleotide polymorphisms (SNPs), copy number variations, and insertions/deletions.

Whole-genome sequencing of exhaled air condensate samples before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Whole-genome sequencing is mainly used to find single nucleotide polymorphisms (SNPs), copy number variations, and insertions/deletions.

Whole-genome sequencing of urine specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Whole-genome sequencing is mainly used to find single nucleotide polymorphisms (SNPs), copy number variations, and insertions/deletions.

Transcriptome sequencing of blood samples before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. The collection of all transcripts, including messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA.

Transcriptome sequencing of tissue samples after surgery in patients with lung cancer/pulmonary nodular and tuberculosis. The collection of all transcripts, including messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA.

Transcriptome sequencing of exhaled air condensate specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. The collection of all transcripts, including messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA.

Transcriptome sequencing of urine specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. The collection of all transcripts, including messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA.

Metabolomics of blood specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Non-target metabolites are generally analyzed qualitatively and quantitatively based on LC-MS technology for metabolites in samples, and identified by matching primary and secondary information with local self-built databases and commercial standard databases.

Metabolomics of tissue samples after surgery in patients with lung cancer/pulmonary nodular and tuberculosis. Non-target metabolites are generally analyzed qualitatively and quantitatively based on LC-MS technology for metabolites in samples, and identified by matching primary and secondary information with local self-built databases and commercial standard databases.

Metabolomics of exhaled air condensate specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Non-target metabolites are generally analyzed qualitatively and quantitatively based on LC-MS technology for metabolites in samples, and identified by matching primary and secondary information with local self-built databases and commercial standard databases.

Metabolomics of urine specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Non-target metabolites are generally analyzed qualitatively and quantitatively based on LC-MS technology for metabolites in samples, and identified by matching primary and secondary information with local self-built databases and commercial standard databases.

Proteomics of blood specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Unlabeled proteomics technology based on the timsTOF Pro ion mobility platform for differential quantitative proteomics analysis using data-dependent acquisition - Synchronous cumulative continuous fragmentation (ddaPASEF) scan mode.

Proteomicstissue samples after surgery in patients with lung cancer/pulmonary nodular and tuberculosis. Unlabeled proteomics technology based on the timsTOF Pro ion mobility platform for differential quantitative proteomics analysis using data-dependent acquisition - Synchronous cumulative continuous fragmentation (ddaPASEF) scan mode.

Proteomics of exhaled air condensate specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Unlabeled proteomics technology based on the timsTOF Pro ion mobility platform for differential quantitative proteomics analysis using data-dependent acquisition - Synchronous cumulative continuous fragmentation (ddaPASEF) scan mode.

Proteomics of urine specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Unlabeled proteomics technology based on the timsTOF Pro ion mobility platform for differential quantitative proteomics analysis using data-dependent acquisition - Synchronous cumulative continuous fragmentation (ddaPASEF) scan mode.

forced expiratory volume in one second (FEV1) for lung volume

carbon monoxide diffusion capacity (DLCO) for pulmonary diffusion function.

St. George's Respiratory Questionnaire total score(0-3989.4), St. George's Respiratory Questionnaire symptoms score(0-662.5); St. George's Respiratory Questionnaire impacts score(0-2117.8); St. George's Respiratory Questionnaire activity score(0-1209.1). The higher the score, the worse the lung.

Serum tumor markers including Carcinoembryonic Antigen (ug/L), Cytokeratin 19 fragment , Squamous Cell Carcinoma Antigen(ug/L), Nervous specific enolase (U/mL), Tissue Polypeptide Specific Antigen(ug/L), Cancer antigen 125 (U/mL), Cancer antigen 15-3 (U/mL), Bombesin (U/mL), The stomach secrete ty (U/mL), β2-microglobulin (U/mL).

Etiological detection including Mycoplasma, Chlamydia, Viruses, Bacteria (especially Mycobacterium tuberculosis), and Fungi. (Positive/Negative)

Wuhan Union Hospital, China

Department of Respiratory and Critical Care Medicine

Rare Diseases

Recurrence

Glioblastoma

Glioma

Astrocytoma

Neoplasms, Neuroepithelial

Neuroectodermal Tumors

Neuroectodermal Tumors, Primitive

Neoplasms, Germ Cell and Embryonal

Neoplasms by Histologic Type

Neoplasms, Glandular and Epithelial

Neoplasms, Nerve Tissue

Neuroepithelioma

Antineoplastic Agents

Hematinics

Bevacizumab

Liver Extracts

Nimustine

Antineoplastic Agents, Immunological

Angiogenesis Inhibitors

Growth Inhibitors

Patients are treated with bevacizumab and nimustine. Every 6 weeks is defined as one therapeutic cycle. Adverse effect is evaluated according to the Common Terminology Criteria for Adverse Events (CTCAE; version 4.03). Hematologic toxicity is evaluated every 2 weeks. Liver function, renal function, and electrolytes are assessed every 4-6 weeks. Platelet should be no less than 100\*10\^9/L and neutrophil count should be no less than 1.5\*10\^9/L.

Bevacizumab is administered intravenously at 5mg/kg every 3 weeks.

Nimustine is administered intravenously at 90mg/m\^2 to 110mg/m\^2 every 6 weeks.

Yu Yao, MD, PhD

Daoying Geng, MD

Xiaojie Ding, MD

Jianbo Wen, MD

The purpose of this study is to determine whether bevacizumab and nimustine are effective in the treatment of recurrent high grade glioma and to explore whether there is any subgroup being sensitive to this therapeutic protocol.

Although anti-angiogenesis therapy for glioblastoma(GBM) are showing promise, GBMs often develop resistance to treatment within months or weeks after salvage therapy. There are still no effective markers to predict the response rate to bevacizumab.

So the investigators initiate a single-arm Phase II study to evaluate the efficacy and tolerability of bevacizumab and nimustine regimen and to explore the predictive markers in patients with recurrent high-grade glioma.

Bevacizumab and Nimustine in Patients With Recurrent High Grade Glioma

Phase II Study of Bevacizumab and Nimustine in Patients With Recurrent High Grade Glioma

Response will be evaluated according to the Response Assessment in Neuro-Oncology(RANO) criteria.Imaging Data (postcontrast T1W,T2/FLAIR),clinical symptoms and corticosteroid use will be collected in each participant and response assessment will be performed by one neurosurgeon and one neuroradiologist.

Progression disease will be evaluated according to the RANO criteria

All toxicities will be assessed and graded according to CTCAE v4.03

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Influence of Biopsy Technique on Moleculargenetic Tumor Characterisation in NSCLC	NCT03971175	Carcinoma, Non-... Pathology, Mole...	Forceps biopsy Cryobiopsy	18 Years -	University Hospital Tuebingen
Development of an Artificial Intelligence System for Intelligent Pathological Diagnosis and Therapeutic Effect Prediction Based on Multimodal Data Fusion of Common Tumors and Major Infectious Diseases in the Respiratory System Using Deep Learning Technology.	NCT05046366	Artificial Inte... Deep Learning Pathology, Mole... Medical Informa... Database Lung Cancer Pulmonary Tuber... Covid19		18 Years - 90 Years	Wuhan Union Hospital, China
Bevacizumab and Nimustine in Patients With Recurrent High Grade Glioma	NCT02698280	Glioblastoma	Bevacizumab Nimustine	18 Years - 75 Years	Huashan Hospital

Spots Global Cancer Trial Database for pathology, molecular