Neoplasms

Digestive System Diseases

All Conditions

Rare Diseases

Esophageal Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Head and Neck Neoplasms

Gastrointestinal Diseases

Esophageal Diseases

Esophageal Cancer

retrospective

Christina Muijs, Dr.

H. Rutten, MD

M. Berbee, MD, PHD

P. van Rossum, MD, PHD

H.W.M. van Laarhoven, MD, PHD

J. Nuyttens, MD, PHD

B. Wijnhoven, MD, PHD

Yvonne Klaver, MD, PHD

Christina Muijs, MD, PHD

C. T. Muijs, MD, PHD

J. A. Langendijk, Prof., MD, PHD

B. van Etten, MD, PHD

J. J. de Haan, MD, PHD

Meindert Sosef, MD

G Meijer, PHD

The aim of this project is to develop and validate NTCP-models for complications after nCRT (and surgical resection), that can be uses for model-based dose optimization for PhRT and PRT, and for model-based selection for PRT, in patients with esophageal cancer

Plan of investigation

In the present project we aim to evaluate two potential strategies to predict and consider a broad spectrum of complications for radiotherapy plan optimization and patient selection:

A comprehensive set of separate NTCP-models for several cardiopulmonary and post-operative complications after nCRT, which results in an NTCP-profile.

A single multivariable NTCP-model for a so-called Complication Sum Score (CSS), which is a single composite complication endpoint including all complications after nCRT, weighted depending on severity and impact.

These prediction models can be used for RT plan optimization and patient selection.

In order to develop these prediction models the project consortium brings together existing prospective data registries for esophageal cancer, namely the Dutch Upper GI Cancer Audit (DUCA) and the Dutch Cancer Registry (NKR) (as well as the POCOP study).

The data of these registries will be linked to a retrospective radiotherapy database using the ProTRAIT infrastructure, resulting in a dataset of over 2000 patients.

In this retrospective cohort study, we will focus on esophageal cancer patients who were treated with nCRT, and for whom an esophagectomy was intended. We will include the patients who received nCRT and the esophagectomy in one of the participating centers. Patients who only received nCRT in one of the participating centers and for whom surgical resection was omitted will be included as well. Detailed eligibility criteria can be found in that section.

Eligible patients will be identified by the Dutch Cancer Registry. The surviving patients will be asked to provide informed consent for participation in this study, permission to combine the different data registries and for future use of their data. Patients who died will be included as well, unless they objected agains the use of there data in the participating centers.

Of the participating patients, the radiotherapy plan-CT and treatment plans (i.e. RTplan, RT struct, RT dose), will be pseudo-anonymized, uploaded and transferred to the UMCG data platform, by the participating radiotherapy centers.

A third trusted party, Zorg TTP, will be responsible for the pseudonymization of the three data sources; the Dutch upper GI cancer audit (DUCA), the Dutch Cancer Registry (NKR) and the RTregistry. After that the linkage will be performed at the UMCG.

Prediction Models for Complications After CRT in Esophageal Cancer

Postoperative complications (graded by ECCG classification (3) ):

Atrial fibrillation ≥grade 2

Pneumonia ≥grade 2

Respiratory failure requiring re-intubation

Anastomotic leakage ≥ grade 2

Pericardial effusion

Myocardial infarction and other acute coronary events

Heart failure Pericardial effusion

Myocardial infarction and other acute coronary events

Heart failure

Atrial fibrillation

Complication Sum Score, that will be determined in Delphi consensus

Patient-rated fatigue / daily functioning

Hospitalization after esophagectomy (days)

Intensive care unit stay after esophagectomy (days)

Dutch Cancer Society

Comprehensive Cancer Centre The Netherlands

Dutch Institute for Clinical Auditing (DICA)

ZorgTTP

University Medical Center Groningen

Stomach Neoplasms

Stomach Diseases

Stomach Cancer

The Source tool is used by care givers for informing patients about the outcomes of treatment. Prediction models are developed and built in the website in order to generate a personalized prediction of the outcomes: survival, toxicity and/or complications and HRQL. These predictions are visualized in clear and comprehensible graphs with a broad variation of options available for tailoring of the visualizations.

In order for care givers to be able to use this tool effectively, we designed the Source training. This communication skills training is comprised of an e-learning, two face-to-face group sessions and an individual booster session. Aside from an instruction video on the navigation within the Source tool, the e-learning consists of theory and tips and tricks on how to inform patients and communicate risks. The face-to-face components of the training are focused on getting the skilled use of the source tool into practice, by receiving personal feedback on the performance.

The Source tool aims to assist health care providers with giving evidence based information about treatment outcomes by providing visualizations of personalized predictions of treatment outcomes for different treatments of oesophageal and gastric cancer. These predictions result from recently designed and updated prediction models on the survival, toxicity, complications and health related quality of life of oesophageal and gastric cancer patients.

The training aims to teach participants to inform patients about outcomes of treatment. The training is placed in the context of shared decision making (SDM), following the 4-step model by Stiggelbout et al., 2015.8 The training is provided in small groups (3-6 participants) by an experienced trainer and supported by professional actors. The training aims to address knowledge, attitude and skills.

Tool and training

Loïs F. van de Water, MSc.

Hanneke WM van Laarhoven, Prof. Dr.

Loïs F van de Water, MSc.

Hanneke WM van Laarhoven, MD, PhD

H WM van Laarhoven, Prof. Dr.

The overarching aim of the programme 'Stimulating evidence based, personalized and tailored information provision to improve decision making after oesophagogastric cancer diagnosis' (SOURCE) is to provide oesophagogastric cancer patients at all disease stages with evidence based and personalized information about survival, treatment-related side-effects and/or complications and health related quality of life, tailored to patients' specific information needs, to facilitate informed decision making about treatment and thereby optimize personal care and outcomes.

For this purpose the Source tool and training were designed. The Source tool is a prediction model based website to be used by care givers for informing patients about the outcomes of treatment. The Source training for care givers is designed to learn care givers how to inform patients effectively, especially about the outcomes of treatment.

The overarching aim of the programme 'Stimulating evidence based, personalized and tailored information provision to improve decision making after oesophagogastric cancer diagnosis' (SOURCE) is to provide oesophagogastric cancer patients at all disease stages with evidence based and personalized information about survival, treatment-related side-effects and/or complications and health related quality of life, tailored to patients' specific information needs, to facilitate informed decision making about treatment and thereby optimize personal care and outcomes.

For this purpose the Source tool and training were designed. The Source tool is a prediction model based website to be used by care givers for informing patients about the outcomes of treatment. The Source training for care givers is designed to learn care givers how to inform patients effectively, especially about the outcomes of treatment.

The primary aim of the SOURCE trial is to investigate the effect of the tool and training on the (numerical) precision of information about outcomes of treatment in the treatment information consultation. Secondary outcomes include: patients' satisfaction, evaluation and knowledge of the information provided by the health care provider, evaluation of the decision made and health related quality of life.

A pragmatic stepped wedge design will be used to test the effect of intervention. 21 health care providers will include 3 patients as control measurements (before intervention) and 3 patients as intervention measurements (after intervention). Participating centers will be divided into geographical subgroups, in which the transition period (in which the intervention will take place) will be spread across time.

All oesophageal and gastric cancer patients scheduled for a treatment information consultation with a participating health care provider are suitable for inclusion and will be approached for participation in the SOURCE study. Measurements include audio recordings of the treatment information consultation and questionnaires filled in by patients and health care providers at different moments in time. Audio recordings will be scored and analyzed based on a study specific coding scheme.

The investigators estimate the physical burden/risk of this study to be negligible.

Evidence Based Information Provision to Improve Decision Making After Oesophagogastric Cancer Diagnosis (SOURCE)

Stimulating Evidence Based, Personalized and Tailored Information Provision to Improve Decision Making After Oesophagogastric CancEr Diagnosis - a Pragmatic Stepped Wedge Trial-

Health care providers' observed use of the Source tool Min. value: 0 Max. value:1 Value 0 means absence of Source tool in consultation, Value 1 means presence of Source tool in conversation

Health care providers' evaluation of the Source tool, training and e-learning Min value: 1 Max value: 10 Higher scores mean better evaluations

The SOURCE observational scale is developed to primary answer the following question: Does the combination of the tool and the training improve observed (numerical) precision of information about treatment outcomes in clinical consultations? Min. value: 1 Max. value:4 Higher scores mean higher (numerical) precision

Health care providers' intention to inform patients about treatment outcomes using numbers (effect training only)

Patient and physician satisfaction with communication and decision making

Patients' evaluation of the decision made

Patients' evaluation and knowledge of the information provided by the health care provider

Patients' health related quality of life, cancer specific

Patients' health related quality of life, gastro-intestinal cancer specific

Patients' trust in the health care provider

Patients' helplessness/hopelessness + fighting spirit

Frequency of explicitly personalizing treatment outcome information, based on audio tapes Min. value: 0 Max. value: endless Higher values mean more utterances of personalization

Frequency of explicitly tailoring treatment outcome information, based on audio tapes Min. value: 0 Max. value: endless Higher values mean more utterances of tailoring

Frequency of explicitly showing visualizations treatment outcome information, based on audio tapes Min. value: 0 Max. value: endless Higher values mean more utterances of showing visualizations

Presence of time frame when giving treatment outcome information, in utterances coded for the primary outcome Min. value: 0 Max. value: 1 Value 1 means time frame present, value 0 means time frame absent

Treatment outcome category of health care providers' treatment outcome information, in utterances coded for the primary outcome Values: Survival, Health-related Quality of Life, Side effects and complications, Progression of tumor Non-ordinal variable

Person taking the initaitive for the health care provider's treatment outcome information, in utterances coded for the primary outcome Values: Health care provider, Patient Non-ordinal variable

Health care providers' (numerical) precision of treatment outcome information measured in simulated patient consultations (effect intervention) Min. value: 1 Max. value:4 Higher scores mean higher (numerical) precision

Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)

Principal Investigator

Urinary Tract, Sexual Organs, and Pregnancy Conditions

Gland and Hormone Related Diseases

Symptoms and General Pathology

Ovarian Neoplasms

Genital Diseases, Female

Endocrine Gland Neoplasms

Cysts

Ovarian Cysts

Genital Neoplasms, Female

Urogenital Neoplasms

Endocrine System Diseases

Genital Diseases

Adnexal Diseases

Ovarian Diseases

Gonadal Disorders

Urogenital Diseases

Female Urogenital Diseases

Pregnancy Complications

Female Urogenital Diseases and Pregnancy Complications

Ovarian Cancer

Standardized transvaginal ultrasound examination

Dirk Timmerman, MD, PhD

Wouter Froyman, MD

Dirk Timmerman, PhD

Tom Bourne, MD, PhD

Ben Van Calster, MSc, PhD

Ignace Vergote, MD, PhD

Lil Valentin, MD, PhD

Antonia C Testa, MD, PhD

Sabine Van Huffel, MSc, PhD

The ability of different methods to discriminate between benign and malignant adnexal masses has been compared in a meta-analysis showing that the IOTA Simple Rules and the IOTA logistic regression model LR2 were superior to RMI and to all other methods for predicting malignancy in an adnexal mass included in the meta-analysis. However, a fair comparison of methods requires them to be applied on the same tumor population.

The general objective of this study is to prospectively validate the Simple Rules, ADNEX, the Simple Rules risk model, LR2, and RMI on a large multicenter dataset to allow direct comparison of these tools.

IOTA7 is an international multicenter prospective observational study including different types of ultrasound centers and examiners with different levels of ultrasound experience. In total, about 1700 adnexal masses with histological outcome will be included in IOTA 7.

Ovarian cancer is a common and lethal disease for which early detection and treatment in high volume centers and by specialized clinicians is known to improve survival. Hence, accurate methods to preoperatively characterize the nature of an ovarian tumor are pivotal.

The best ultrasound method for discrimination between benign and malignant adnexal masses is subjective assessment, i.e. subjective evaluation of ultrasound findings. Subjective assessment, however, requires a very experienced ultrasound examiner. More objective methods may be preferred by less experienced examiners who are not confident with using subjective assessment. The Risk of Malignancy Index (RMI) is one such method. There are also more recently developed methods. The International Ovarian Tumor Analysis (IOTA) group have created logistic regression models (LR1, LR2, and the ADNEX model) including clinical and ultrasound information to calculate the likelihood of malignancy in adnexal masses.

The IOTA group has also suggested simple ultrasound rules that can be used to classify adnexal masses as benign or malignant.

The ability of different methods to discriminate between benign and malignant adnexal masses has been compared in a meta-analysis showing that the IOTA Simple Rules and the IOTA logistic regression model LR2 were superior to RMI and to all other methods for predicting malignancy in an adnexal mass included in the meta-analysis. However, a fair comparison of methods requires them to be applied on the same tumor population.

The general objective of this study is to prospectively validate the Simple Rules, ADNEX, the Simple Rules risk model, LR2, and RMI on a large multicenter dataset to allow direct comparison of these tools. The patients will be examined by operators of varying levels of experience, such that the investigators can study how experience might affect diagnostic performance.

IOTA7 is an international multicenter prospective observational study including different types of ultrasound centers and examiners with different levels of ultrasound experience. In total, about 1700 adnexal masses with histological outcome will be included in IOTA 7.

Patients with a known or suspected adnexal mass examined with transvaginal (or transrectal if vaginal is not possible) ultrasound by an IOTA certified ultrasound examiner and confirmed to have an adnexal mass judged not to be physiological but likely to undergo surgery (primarily planned for surgical management based on subjective assessment by the ultrasound investigator) will be recruited consecutively Upon inclusion in the study, an oral or written (depending on the requirements of the local Ethics Committee) informed consent is obtained from the patient.

Data collection is done through the web-based clinical data miner (CDM) software. Data are stored on a secure server.

First, clinical information about the patient is entered into CDM. Second, the ultrasound examiner provides a diagnosis based on subjective assessment, and notes the suggested management. Third, detailed ultrasound information needed for the different models to be validated is entered. When these data have been frozen (so they can no longer be changed) the examiner gets access to the results of the Simple Rules and ADNEX. The ultrasound examiner then records whether these results make him/her change the management suggested on the basis of subjective assessment. If so the new management is specified.

Gold standard for validation of discriminative ability and calibration is the histology of the adnexal mass within 180 days after the ultrasound examination following surgical removal by laparotomy or laparoscopy as considered appropriate by the surgeon. In case of malignancy, the stage of the malignant tumors using the classification of the International Federation of Gynecology and Obstetrics (FIGO) is noted.

Prospective Validation and Comparison of Different Ultrasound Methods for Discrimination Between Benign and Malignant Ovarian/Tubal Masses Prior to Surgery

Prospective Validation and Comparison of Subjective Assessment, the International Ovarian Tumor Analysis (IOTA) ADNEX Model, Logistic Regression Model LR2, Simple Rules and the Risk of Malignancy Index (RMI) for Discrimination Between Benign and Malignant Adnexal Masses in the Hands of Ultrasound Examiners With Different Levels of Experience

KU Leuven

Prof. Dr.

Respiratory Tract (Lung and Bronchial) Diseases

Infections

Wounds and Injuries

Lung Neoplasms

Pneumonia

Radiation Pneumonitis

Communicable Diseases

Respiratory Tract Infections

Lung Diseases

Respiratory Tract Diseases

Lung Diseases, Interstitial

Lung Injury

Radiation Injuries

Lung cancer patients treated with thoracic radiation therapy

Xianglin Yuan, MD, PhD

Yang Tang, MD, PhD

Radiation pneumonitis is the most common complication and the major dose-limiting toxicity associated with radiotherapy, which can cause poor quality of life or life-threatening symptoms and might hinder the tumor-controlling effects of radiotherapy. Consequently, establishing reliable predictors for the occurrence of RP is of great significance such that the therapeutic effects of RT can be maximized while minimizing its adverse effects. The aim of this study is to figure out the biological prediction models of radiation pneumonitis.

1. Establish a clinical data base for the lung cancer patients treated with radiotherapy in Tongji Hospital.

   Data including age, gender, smoking state, KPS, pulmonary function, disease history, histology, stage, surgery, chemotherapy, dosimetric parameters of radiotherapy were collected.

   Peripheral blood was collected from all patients enrolled. All patients enrolled in this study are examined during and one month after radiotherapy. Then, the patients are followed every three months for the first year and every six months thereafter. At each follow-up visits, all patients are asked to undergo a chest X-ray or CT and clinical information, including symptoms, is collected. RP is graded by two radiation oncologists according to the Common Terminology Criteria for Adverse Events 4.0.
2. Select candidate genes and pathways which may be associated with radiation pneumonitis and identify their tagSNPs by Haploview.
3. Use Kaplan-Meier and Cox model to analyze the association of all the factors collected with radiation pneumonitis.
4. Establish a biological prediction models for radiation pneumonitis based on the statistics analysis

Study on the Biological Prediction Models of Radiation Pneumonitis

Huazhong University of Science and Technology

Professor, Chief Physician

Three-dimensional conformal radiotherapy (3DCRT)

3DCRT

Zefen Xiao, MD

Wenyang Liu, MD

Radiotherapy is one of the main treatments for locally advanced esophageal carcinoma (EC). The accuracy of the existing imaging methods in diagnosing and predicting therapeutic efficacy is disappointing, which increases the difficulty in clinical decision-making. In this study, based on a continuous cohort of EC treated with radiotherapy, the clinical and pathological factors of the patients are used to classify them into the appropriate therapeutic group. By multiple liquid biopsy technologies, combining with radiomics, we intend to construct prediction models of prognosis, therapeutic effect and toxicity. The aim of this RWS is to provide appropriate individualized regimen, further optimize the treatment mode based on precision radiotherapy and improve the outcome and quality of life of EC patients.

Radiotherapy is one of the main treatments for locally advanced esophageal squamous cell carcinoma (ESCC). The guidelines recommend neoadjuvant concurrent chemoradiotherapy plus surgery for resectable or potentially resectable patients; for unresectable patients, definitive chemoradiotherapy is the standard treatment. However, due to the complexity of the biological behavior of esophageal cancer (EC) and individual differences, fully complying with guideline recommendations in clinical practice is difficult and idealized. The results of prospective clinical trials are difficult to meet the demand of clinical diagnosis and treatment, thus, carrying out high-quality real-world study (RWS) is necessary.

Three-dimensional conformal radiotherapy (3DCRT) for unresectable EC yields 5-year OS rates of 34%-45.6%, which is an improvement over the rates reported in the RTOG 85-01 and 94-05 studies. Even so, there is still room for improvement of local control rate and overall survival. The accuracy of the existing imaging methods \[computed tomography (CT), magnetic resonance imaging (MRI), endoscopic ultrasonography (EUS), endoscopic ultrasonography (EUS), as well as positron-emission tomography (PET)-CT, etc.\] in diagnosing and predicting therapeutic efficacy is disappointing, which increases the difficulty in clinical decision-making. It is worthy to investigate an appropriate individualized radiation regimen based on different treatment sensitivity.

In this study, based on a continuous cohort of EC treated with radiotherapy, the clinical and pathological factors of the patients are used to classify them into the appropriate therapeutic group. Collect the blood and saliva samples before, during and after radiotherapy; the remaining diagnostic biopsy tissue samples. By using multiple liquid biopsy technologies \[microbial flora, circulating tumor DNA (ctDNA), genome, RNA, and immunophenotype, ect.\], combining with radiomics, construct prediction models of prognosis, therapeutic effect and toxicity. The aim of this RWS is to provide appropriate individualized regimen, further optimize the treatment mode based on precision radiotherapy and improve the outcome and quality of life of EC patients.

The blood and saliva samples before, during and after radiotherapy; the remaining diagnostic biopsy tissue samples.

Real-world Study on Comprehensive Treatment of Esophageal Cancer Based on Precision Radiotherapy

potential biomarkers in tumor samples and blood samples

measurement basing on EORTC (Quality of life of patients with oesophageal cancer) QLQ-C30 tables

measurement basing on EORTC (Quality of life of patients with oesophageal cancer) QLQ-OES18 tables

Zefen Xiao

Professor

Acute Graft Versus Host Disease

Radiotherapy

CT Muijs, MD PhD

Motive:

In order to improve the treatment technique, a comprehensive follow-up program is needed to obtain all relevant patient, treatment and toxicity data from esophageal cancer patients.

Goal:

To set-up and maintain a database containing treatment results in terms of tumor control, side effects, complications and patient-reported quality of life.

A standard database of patients receiving photon treatment will be created. These data are then linked to dose-volume data of radiotherapy, with the aim to build prediction models for both tumor control and toxicity after radio (chemo) therapy that can later be used for selecting patients for proton treatment.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Prediction Models for Complications After CRT in Esophageal Cancer	NCT06366828	The Aim of This...	retrospective	18 Years - 100 Years	University Medical Center Groningen
Evidence Based Information Provision to Improve Decision Making After Oesophagogastric Cancer Diagnosis (SOURCE)	NCT04232735	Oesophageal Can... Gastric Cancer	Tool and traini...	18 Years -	Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Prospective Validation and Comparison of Different Ultrasound Methods for Discrimination Between Benign and Malignant Ovarian/Tubal Masses Prior to Surgery	NCT02847832	Patients With A...	Standardized tr...	-	KU Leuven
Study on the Biological Prediction Models of Radiation Pneumonitis	NCT02490319	Radiation Pneum...		18 Years - 80 Years	Huazhong University of Science and Technology
Real-world Study on Comprehensive Treatment of Esophageal Cancer Based on Precision Radiotherapy	NCT05543057	Esophageal Canc...	3DCRT	18 Years - 90 Years	ChineseAMS
Evidence Based Information Provision to Improve Decision Making After Oesophagogastric Cancer Diagnosis (SOURCE)	NCT04232735	Oesophageal Can... Gastric Cancer	Tool and traini...	18 Years -	Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Study on the Biological Prediction Models of Radiation Pneumonitis	NCT02490319	Radiation Pneum...		18 Years - 80 Years	Huazhong University of Science and Technology
Standard Follow-Up Program (SFP) for Patients With Esophageal Cancer	NCT02454790	Esophageal Canc...	Radiotherapy	18 Years -	University Medical Center Groningen
Evidence Based Information Provision to Improve Decision Making After Oesophagogastric Cancer Diagnosis (SOURCE)	NCT04232735	Oesophageal Can... Gastric Cancer	Tool and traini...	18 Years -	Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Real-world Study on Comprehensive Treatment of Esophageal Cancer Based on Precision Radiotherapy	NCT05543057	Esophageal Canc...	3DCRT	18 Years - 90 Years	ChineseAMS
Standard Follow-Up Program (SFP) for Patients With Esophageal Cancer	NCT02454790	Esophageal Canc...	Radiotherapy	18 Years -	University Medical Center Groningen

Spots Global Cancer Trial Database for prediction models