Symptoms and General Pathology

All Conditions

Neoplasms

Urinary Tract, Sexual Organs, and Pregnancy Conditions

Rare Diseases

Recurrence

Carcinoma

Carcinoma, Renal Cell

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

Adenocarcinoma

Kidney Neoplasms

Urologic Neoplasms

Urogenital Neoplasms

Urogenital Diseases

Female Urogenital Diseases

Pregnancy Complications

Female Urogenital Diseases and Pregnancy Complications

Kidney Diseases

Urologic Diseases

Male Urogenital Diseases

Renal Cell Carcinoma

Clear Cell Renal Cell Carcinoma

Yingjie Xv

This study aims to preoperatively predict DFS of patients with localised ccRCC using a deep learning prognostic model based on enhanced contrast CT images, validate it's predictive ability in multicentre data and compare it's predictive ability with traditional models.

Contrast-enhanced CT-based Deep Learning Model for Preoperative Prediction of Disease-free Survival (DFS) in Localized Clear Cell Renal Cell Carcinoma (ccRCC)

Urology Department of the First Affiliated Hospital of Chongqing Medical University

the interval from the date of surgery to disease recurrence, all-cause mortality or the last visit

Mingzhao Xiao

Urology Department

Digestive System Diseases

Colorectal Neoplasms

Intestinal Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Colonic Diseases

Intestinal Diseases

Rectal Diseases

Colorectal cancer patients are enrolled when patients were underwent surgeries.

Jipeng Li

To compare the differences of clinical pathological, treatment and prognosis in the guided subgroups in colorectal cancer, the investigator enrolled all the colorectal cancer patients who underwent surgery and were hospitalized in the Xijing hospital.

Colorectal Cancer Dataset in Xijing Hospital From 2011

The investigators aim to explore the clinical characteristics of colorectal cancer patients in the Xijing hospital.

Xijing Hospital

Skin and Connective Tissue Diseases

Breast Neoplasms

Phyllodes Tumor

Breast Diseases

Skin Diseases

Sarcoma

Neoplasms, Connective and Soft Tissue

Phyllodes Tumor of the Breast

Soft Tissue Sarcoma

Patients diagnosed with phyllodes tumor of breast

Patient medical imaging materials including ultrasound, mammography, CT, MRI

imaging

Yan Nie, Prof.Dr.

Feng Ye, Prof.Dr.

Hui Mai, Prof.Dr.

Yu Tan, Prof.Dr.

Breast phyllodes tumor (PT) is a rare fibroepithelial tumor, accounting for 1% to 3% of all breast tumors, categorized by the WHO into benign, borderline, and malignant, based on histopathology features such as tumor border, stromal cellularity, stromal atypia, mitotic activity and stromal overgrowth. Malignant PTs account for 18%-25%, with high local recurrence (up to 65%) and distant metastasis rates (16%-25%). Benign PT could progress to malignancy after multiple recurrences. Therefore, Early, accurate diagnosis and identification of therapeutic targets are crucial for improving outcomes and survival rates.

In recent years, there has been growing interest in the application of artificial intelligence (AI) in medical diagnostics. AI can integrate clinical information, histopathological images, and multi-omics data to assist in pathological and clinical diagnosis, prognosis prediction, and molecular profiling.AI has shown promising results in various areas, including the diagnosis of different cancers such as colorectal cancer, breast cancer, and prostate cancer. However, PT differs from breast cancer in diagnosis and treatment approach. Therefore, establishing an AI-based system for the precise diagnosis and prognosis assessment of PT is crucial for personalized medicine.

The research team, led by Dr. Nie Yan, is one of the few in Guangdong Province and even nationally, specializing in PT research. Their team has been conducting research on the malignant progression, metastasis mechanisms, and molecular markers for PT. The team has identified key mechanisms, such as fibroblast-to-myofibroblast differentiation, and the role of tumor-associated macrophages in promoting this differentiation. They have also identified molecular markers, including miR-21, α-SMA, CCL18, and CCL5, which are more accurate in predicting tumor recurrence risk compared to traditional histopathological grading.

The project has collected high-quality data from nearly a thousand breast PT patients, including imaging, histopathology, and survival data, and has performed transcriptome gene sequencing on tissue samples. They aim to build a comprehensive multi-omics database for breast PT and create an AI-based model for early diagnosis and prognosis prediction. This research has the potential to improve the diagnosis and treatment of breast PT, address the disparities in breast PT care across different regions in China, and contribute to the development of new therapeutic targets.

AI-Assisted System for Accurate Diagnosis and Prognosis of Breast Phyllodes Tumors

Development of an Artificial Intelligence-Based System for Precise Diagnosis and Prognosis of Breast Phyllodes Tumors

no plan to make individual participant data available to other researchers.

The probability of a positive test result, conditional on it being truly positive.

Determine the odds of testing negative in a positive population.

The probability of a negative test result conditional on a true negative.

Determine the odds of testing positive in a negative population.

The ROC curve is a curve based on a series of different dichotomous classifications (cut-off values or decision thresholds), with the rate of true positives (sensitivity) as the vertical coordinate and the rate of false positives (1-specificity) as the horizontal coordinate.

AUC is defined as the area under the ROC curve enclosed with the axes, and the closer the AUC is to 1.0, the more authentic the assay is.

Sun Yat-sen University

Peking University Shenzhen Hospital

Guangdong Provincial Maternal and Child Health Hospital

The Third Affiliated Hospital of Guangzhou Medical University

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Principal Investigator

Blood and Lymph Conditions

Immune System Diseases

Lymphoma

Hodgkin Disease

Lymphosarcoma

Hodgkin Lymphoma

Antineoplastic Agents

All Drugs and Chemicals

Anti-Infective Agents

Antirheumatic Agents

Anti-Inflammatory Agents

Antiemetics

Gastrointestinal Agents

Etoposide

Cytarabine

Cyclophosphamide

Carboplatin

Doxorubicin

Liposomal doxorubicin

Dexamethasone

Prednisone

Vincristine

Cisplatin

Ifosfamide

Dexamethasone acetate

Etoposide phosphate

Bleomycin

Vinblastine

Dacarbazine

Isophosphamide mustard

Procarbazine

Standard first-line treatment that includes 2 cycles of ABVD (doxorubicin, bleomycin, vinblastine, dacarbazine) and involved site radiotherapy of 20 Gy in early clinical stages I or II without risk factors.

Patients below 60 years in intermediate clinical stages I or II with at least one risk factor are treated with 2 cycles of BEACOPP (bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, prednisone) escalated and 2 cycles of ABVD in PET-4 negative cases. Involved site radiotherapy of 30 Gy is indicated in PET-4 positive patients with intermediate stages after chemotherapy.

Patients in advanced stages III or IV and patients in stage IIB with massive mediastinal tumor and/or extranodal disease are treated with 4 or 6 cycles of BEACOPP escalated based on PET-2 negativity or positivity.

Elderly patients in intermediate stages are treated with 2 cycles of ABVD and 2 cycles of AVD without bleomycin and involved site radiotherapy of 30 Gy.

Elderly patients in advanced stages are treated with 2 cycles of ABVD and 4 cycles of AVD without bleomycin

The treatment for patients in relapse up to the age of 65 years is two cycles of platinum based salvage chemotherapy: cisplatin, cytarabine and dexamethasone (DHAP) or ifosfamide, carboplatin, etoposide (ICE) followed by high- dose chemotherapy and autologous stem cell transplantation (ASCT).

Heidi Mocikova, M.D., Ph.D.

Ondrej Havranek, assoc. prof.

Alice Sykorova, M.D., Ph.D.

Vit Prochazka, prof. M.D.

Katerina Klaskova, Ing.

Jan Koren, M.D.

Specific somatic mutations using ctDNA will be analyzed in predefined subgroups of cHL (e.g., age \<60 and ≥ 60 years, EBV). These mutations will be correlated with response to the treatment in the first line, in the relapse, during brentuximab vedotin and/or nivolumab treatment. Circulating tumor DNA will be correlated with the extent of tumor mass and chemo/radiotherapy.

Samples of plasma from peripheral blood will be taken for investigational ctDNA examination during the specific timepoints: at diagnosis, after 2 cycles of initial chemotherapy, at the end of chemotherapy, 3 months after radiotherapy, at the diagnosis of the first relapse, after salvage chemotherapy before ASCT, 3 months after ASCT, at the diagnosis of second relapse and every 3 months during brentuximab vedotin treatment or during nivolumab treatment until progression. The buccal swab for germline DNA extraction will be performed at the time of enrollment into the study. Samples of peripheral blood for EBV-DNA analysis will be obtained from the EBV-positive cHL patients to measure EBV load at the same time-points as ctDNA. Microdissected HRS cells from fresh frozen biopsies at the diagnosis and at the relapse will be used for tumor cells next generation sequencing.

Identification of tumor specific mutation profiles at diagnosis of classical Hodgkin lymphoma Quantitative analysis of ctDNA level during the first-line chemotherapy Identification of tumor specific mutation profiles at relapse of classical Hodgkin lymphoma In vitro functional characterization of identified DNA variants and/or mutations

Prognostic Significance of ctDNA in HL

Prognostic Significance of Circulating Tumor DNA in Hodgkin Lymphoma

Identification of tumor specific mutation profiles at diagnosis of classical Hodgkin:

1. age at diagnosis \< 60 years in comparison to patients with age at diagnosis 60 years and more
2. EBV negative versus EBV positive cases
3. correlation with the first line treatment outcome lymphoma based on ctDNA analysis with correlation to clinical and pathological characteristics

Quantitative analysis of ctDNA level during the first-line chemotherapy:

1. analysis in relation to the type of chemotherapy: BEACOPP escalated vs ABVD
2. dynamics of ctDNA decline in correlation with treatment response

Identification of tumor specific mutation profiles at relapse of classical Hodgkin lymphoma:

1. detection of newly developed mutations in comparison to the initial diagnosis
2. characteristics of mutations in HL tumors refractory to brentuximab vedotin
3. characteristics of mutations in HL tumors refractory to nivolumab

In vitro functional characterization of identified DNA variants and/or mutations:

1. variants with unknown impact on classical HL development
2. variants identified as associated with features analyzed in above mentioned goals

Charles University, Czech Republic

General University Hospital, Prague

University Hospital Olomouc

University Hospital Hradec Kralove

University Hospital, Motol

Interni hematologicka klinika FNKV

Gland and Hormone Related Diseases

Ovarian Neoplasms

Carcinoma, Ovarian Epithelial

Endocrine Gland Neoplasms

Ovarian Diseases

Adnexal Diseases

Genital Diseases, Female

Genital Diseases

Genital Neoplasms, Female

Endocrine System Diseases

Gonadal Disorders

Ovarian Cancer

Ovarian Epithelial Cancer

For pathological evaluation, the omental specimens resected during the IDS were stained with haematoxylin and eosin (H\&E) and reviewed independently by two gynecologic pathologists, both blinded to the clinical data and each other's results. The pathology slide obtained from omentum, usually the site with the most viable tumor, was selected for CRS assessment according to the three-tiered CRS system recommended by 2019 ESMO ovarian cancer guidelines

CRS scoring

The routine blood tests and tumor marker measurements, including CA125, HE4, and inflammation-related serum biomarkers including neutrophils, lymphocytes, monocytes, fibrinogen, and platelets, were conducted within three days before the first NACT.

Routine blood laboratory testing before treatment

A validated prognostic index for the outcome of advanced high-grade serous ovarian cancer (HGSOC) patients undergoing neoadjuvant chemotherapy (NACT) is still lacking. To address this need, we developed an ovarian neoadjuvant chemotherapy prognostic index (ONCPI) to improve predictive accuracy. We analyzed the clinicopathological characteristics of advanced HGSOC patients receiving platinum-based NACT. Blood inflammatory composite markers were calculated and binary-transformed using optimal cutoffs. Omental hematoxylin and eosin (H\&E) stained slides were selected for the assessment of chemotherapy response score (CRS). Logistic regression analysis and Cox proportional hazards regression model were utilized to develop a prognostic index.

1. The clinicopathological data of patients newly diagnosed with high-grade serous ovarian cancer in Sun Yat-sen Memorial Hospital were collected and screened.
2. Statistical analysis of hematological indicators that may be related to inflammation before platinum-based therapy in patients who met the inclusion criteria, including but not limited to: Absolute white blood cell count, absolute neutrophil count, absolute lymphocyte count, absolute monocyte count, platelet count, hemoglobin and fibrinogen were calculated. NLR, MLR, PLR, FLR and SII were calculated, and the correlation between the above indicators and tumor grade, stage, platinum-based drug sensitivity and prognosis was analyzed.
3. CRS scoring was performed using H\&E sections of omentum obtained during IDS surgery.
4. logistic regression and Cox regression were used to analyze the independent risk factors affecting the sensitivity of neoadjuvant platinum-based chemotherapy and the prognosis of patients.
5. K-M analysis and ROC curve were used to analyze the predictive value of NLR combined with CRS ONCPI index for the response of high-grade serous ovarian cancer to platinum therapy.

New Prognostic Index for Neoadjuvant Chemotherapy Outcome in Patients With Advanced High-grade Serous Ovarian Cancer

If IPD is to be shared, it will be limited to the patient's clinicopathological and prognostic data, and the patient's personal information will be protected

After NACT-IDS and postoperative adjuvant chemotherapy, treatment efficacy was assessed according to NCCN guidelines. For primary tumor, patients who relapsed 6 months or more after initial chemotherapy were termed platinum-sensitive. In contrast, patients whose disease recurred in less than 6 months were classified as platinum-resistant.

PFS was calculated from the date of the first NACT until disease progression or death due to any cause.

OS was calculated from the date of the first NACT administration until death due to any cause.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Contrast-enhanced CT-based Deep Learning Model for Preoperative Prediction of Disease-free Survival (DFS) in Localized Clear Cell Renal Cell Carcinoma (ccRCC)	NCT06088134	Clear Cell Rena... Prognostic Canc... Recurrent Renal...		-	First Affiliated Hospital of Chongqing Medical University
Colorectal Cancer Dataset in Xijing Hospital From 2011	NCT06308354	Prognostic Canc... Chemotherapy Nutrition Relat...		18 Years - 100 Years	Xijing Hospital
AI-Assisted System for Accurate Diagnosis and Prognosis of Breast Phyllodes Tumors	NCT06286267	Phyllodes Breas... Artificial Inte... Multiomics Prognostic Canc... Diagnosis	imaging	-	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Prognostic Significance of ctDNA in HL	NCT06263530	Prognostic Canc...		18 Years -	Interni hematologicka klinika FNKV
New Prognostic Index for Neoadjuvant Chemotherapy Outcome in Patients With Advanced High-grade Serous Ovarian Cancer	NCT06120309	Ovarian Cancer Neoadjuvant Che... Prognostic Canc...	CRS scoring Routine blood l...	18 Years - 75 Years	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
AI-Assisted System for Accurate Diagnosis and Prognosis of Breast Phyllodes Tumors	NCT06286267	Phyllodes Breas... Artificial Inte... Multiomics Prognostic Canc... Diagnosis	imaging	-	Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

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