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An extensive body of literature describes anticancer property of dichloroacetate (DCA), but its effective clinical administration in cancer therapy is still limited to clinical trials. The occurrence of side effects such as neurotoxicity as well as the suspicion of DCA carcinogenicity still restricts the clinical use of DCA. However, in the last years, the number of reports supporting DCA employment against cancer increased also because of the great interest in targeting metabolism of tumour cells. Dissecting DCA mechanism of action helped to understand the bases of its selective efficacy against cancer cells. A successful coadministration of DCA with conventional chemotherapy, radiotherapy, other drugs, or natural compounds has been tested in several cancer models. New drug delivery systems and multiaction compounds containing DCA and other drugs seem to ameliorate bioavailability and appear more efficient thanks to a synergistic action of multiple agents. The spread of reports supporting the efficiency of DCA in cancer therapy has prompted additional studies that let to find other potential molecular targets of DCA. Interestingly, DCA could significantly affect cancer stem cell fraction and contribute to cancer eradication. Collectively, these findings provide a strong rationale towards novel clinical translational studies of DCA in cancer therapy.
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http://dx.doi.org/10.1155/2019/8201079 | DOI Listing |
Urol Oncol
September 2025
Faculty of Medicine, Department of Nuclear Medicine, Gazi University, Beşevler Ankara, Turkey.
Background: This study aimed to evaluate the diagnostic performance of Ga-PSMA-11 PET/MRI in prostate cancer (PC) with biochemical recurrence (BCR) after radical prostatectomy (RP). It was also aimed to develop a nomogram to predict PET/MRI positivity.
Methods: The data of 140 PC patients who underwent Ga-PSMA-11 PET/MRI for BCR after RP were retrospectively analyzed.
J Robot Surg
September 2025
Department of Urology, the First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, Anhui, People's Republic of China.
Inguinal hernia represents a clinically significant yet underreported complication of robot-assisted radical prostatectomy (RARP) for localized prostate cancer, with a notably high incidence within the first postoperative year. Despite its adverse impact on quality of life and potential for severe sequelae, predictive tools for this outcome remain limited. To develop and validate the first machine learning (ML)-based clinical prediction model for inguinal hernia within 1 year after RARP, leveraging explainable artificial intelligence (AI) techniques for clinical interpretability.
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August 2025
Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, China.
Introduction: Surgery remains the primary treatment for patients with esophageal cancer (EC), yet postoperative prognosis is often unsatisfactory. Accurate prediction of cancer-specific survival (CSS) can assist clinicians in personalized treatment planning. This study aimed to develop an interactive web-based tool to estimate CSS in patients with T1~3N0~2M0 EC after surgery, based on the log odds of negative lymph nodes/T stage ratio (LONT).
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September 2025
Department of Neurosurgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University, Guangzhou, China.
Background: Primary central nervous system (CNS) germ cell tumors (GCTs) are common neoplasms in the CNS of pediatric and adolescent patients. This study aimed to identify prognostic factors associated with CNS GCTs and establish an effective nomogram for predicting overall survival (OS) in patients with CNS GCTs.
Methods: The development cohort including 1166 CNS GCTs patients was selected from Surveillance, Epidemiology, and End Results (SEER) program between 2000 and 2021.
Front Endocrinol (Lausanne)
September 2025
Ultrasound Diagnostic Center, The First Hospital of Jilin University, Changchun, China.
Objective: This study aims to construct a multimodal radiomics model based on contrast-enhanced ultrasound (CEUS) radiomic features, combined with conventional ultrasonography (US) images and clinical data, to evaluate its diagnostic efficacy in differentiating benign and malignant thyroid nodules (TNs) classified as C-TIRADS 4, and to assess the clinical application value of the model.
Methods: This retrospective study enrolled 135 patients with C-TIRADS 4 thyroid nodules who underwent concurrent US and CEUS before FNA/surgery. From each case, one US image and three CEUS key frames (2s post-perfusion, peak enhancement, 2s post-peak) were selected.