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This trial is no longer recruiting — here are similar active trials
Matched by cancer type/biomarker, currently recruiting.
The purpose of this study is to compare the efficacy of zipalertinib versus placebo in participants with early stage resected non-small cell lung cancer (NSCLC) harboring uncommon epidermal growth factor receptor mutation (EGFRmt).
The overall aim of this project is to help improve the Health Related Quality of Life (HRQOL) of metastatic breast cancer (MBC) patients. There is currently an urgent and unmet need for specific tools to aid in the evaluation of HRQOL in MBC and this project aims to address these needs by developing a comprehensive EORTC questionnaire for assessing the HRQOL of this group. The new module will be used in conjunction with EORTC QLQ-C30 and will provide better measurement of the quality of life issues experienced by women with MBC. Interviews will be conducted with patients from several centres across Europe, guiding the development process in a patient centred way.
This is a Phase III, multicenter, randomized, open-label, global study designed to evaluate the efficacy and safety of inavolisib plus fulvestrant compared with alpelisib plus fulvestrant in patients with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2) -negative, PIK3CA-mutated, locally advanced (LA) or metastatic breast cancer (mBC), who progressed during or after cyclin dependent kinase 4/6i (CDK4/6i)-based therapy. Enrollment for the main study is now complete.
PRO-BOOST-LC is a prospective, multicenter, randomized phase II/III clinical trial for men with localized or locally advanced prostate cancer without lymph node or distant metastases, confirmed using prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT). Radiotherapy is an established curative treatment option for prostate cancer. Several modern radiotherapy strategies can safely deliver high radiation doses to the prostate while limiting dose to surrounding organs. These include stereotactic body radiotherapy (SBRT), high-dose-rate (HDR) brachytherapy, low-dose-rate (LDR) brachytherapy, and combinations of external beam radiotherapy with a prostate boost. However, the optimal dose-escalation strategy for balancing cancer control, treatment-related toxicity, and long-term quality of life remains uncertain in patients staged with modern PSMA PET imaging. The aim of PRO-BOOST-LC is to compare definitive SBRT monotherapy with whole-gland prostate boost strategies delivered after a short course of external beam radiotherapy. Participants will be randomly assigned, according to center capability and patient-level technical suitability, to one of the protocol-defined treatment options. The control group receives SBRT monotherapy. The experimental groups receive external beam radiotherapy followed by one of three whole-gland boost techniques: HDR brachytherapy, LDR brachytherapy, or single-fraction SBRT boost. The primary objective is to determine whether assignment to a prostate boost strategy improves failure-free survival compared with SBRT monotherapy. Failure-free survival includes biochemical recurrence, local or regional progression, distant metastases, progression-driven salvage treatment, or death from any cause. Key secondary outcomes include metastasis-free survival, overall survival, physician-reported treatment-related toxicity, and patient-reported quality of life, including urinary, bowel, and sexual function. Participants will undergo baseline clinical evaluation, PSA testing, prostate MRI, PSMA PET/CT, and quality-of-life assessments. After treatment, participants will be followed regularly with clinical assessments, PSA testing, toxicity evaluation, patient questionnaires, and imaging when clinically indicated. The study is designed to provide long-term evidence on how best to use modern radiotherapy dose escalation for patients with PSMA-staged localized or locally advanced prostate cancer.
Breast cancer is a malignant tumor with the highest morbidity and mortality among women worldwide. Accurate staging of axillary lymph nodes is critical for metastatic assessment and decisions regarding treatment modalities in breast cancer patient. Among patients who underwent sentinel lymph node biopsy, about 70 % of the patients had negative pathological results and in other words, these 70 % of the patients received unnecessary surgery. At present, imaging and pathological diagnosis is the main measure of lymph node metastasis in breast cancer. However, limitations remained. Artificial intelligence, including deep learning and machine learning algorithms, has emerged as a possible technique, which can make a more accuracy prediction through machine-based collection, learning and processing of previous information, especially in radiology and pathology-based diagnosis. With the intensification of the concept of precision medicine and the development of non-invasive technology, the investigators intend to use the artificial intelligence technology to develop a serum and tissue-based predictive model for sentinel lymph node metastasis diagnosis combined with imaging and pathological information, providing specific, efficient and non-invasive biological indicators for the monitoring and early intervention of lymph node metastasis in patient with breast cancer. Therefore, the investigators retrospectively include serum samples from early breast cancer patients undergoing sentinel lymph node biopsy, including a discovery cohort and a modeling cohort. Metabolites were detected and screened in the discovery cohort and then as the target metabolites for targeted detection in the modeling cohort. Combined with preoperative imaging and pathological information, a prediction model of breast cancer sentinel lymph node metastasis based on serum metabolites would be established. Subsequently, multi-center breast cancer patients will prospectively be included to verify the accuracy and stability of the model.
Original English text from ClinicalTrials.gov
✓ Qualifies
✗ Disqualifies
Simplified criteria — AI translation
Shantou Central Hospital
Shantou, China
Xiaorong Lin, Dr.
Contact information from ClinicalTrials.gov. Contact in English.
Data from ClinicalTrials.gov. AI-assisted translation, last sync: 7/1/2026.
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