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Purpose: To investigate the dose-effect relationship between the dose-volume parameters of residual gross tumor volume (GTV) and clinical prognosis in MRI image-guided adaptive brachytherapy (IGABT) of patients with locally advanced cervical cancer in our center.
Materials And Method: The clinical data of 93 patients with locally advanced cervical squamous cell cancer who received external beam radiotherapy (EBRT) combined with IGABT ± chemotherapy in our center were retrospectively analyzed. The disease stage, overall treatment time (OTT), chemotherapy, and the dose-volume parameters D, D, and D of GTV, the intermediate-risk clinical target volume (CTV), and the high-risk clinical target volume (CTV) of the patients were statistically analyzed. Kaplan-Meier and uni- and multivariable Cox regression analyses were used to analyze 2‑year overall survival (OS), progression-free survival (PFS), and local control rate (LC). A probit model was employed to assess the dose-effect relationship between the volume and dose-volume parameters of GTV and 2‑year OS, PFS, and LC.
Results: The median follow-up time was 19.6 months and 2‑year OS, PFS, and LC were 79.6%, 68.8%, and 94.6%, respectively. CTV D was an independent influencing factor for 2‑year PFS (P = 0.041); GTV volume was an independent factor for 2‑year OS, PFS, and LC (P < 0.001). The probit model showed that at GTV volume < 32.86 cm, the expected 2‑year LC was > 90%; at GTV D > 129.12 Gy, the expected 2‑year OS was > 90%.
Conclusion: Both the volume and dose-volume parameters of GTV are promising predictors in assessment of IGABT prognosis of cervical cancer.
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http://dx.doi.org/10.1007/s00066-022-02000-6 | DOI Listing |
J Cancer Res Clin Oncol
September 2025
Cancer Treatment and Nuclear Cardiology Department, Al Azhar University, Cairo, Egypt.
Background: High-dose-rate (HDR) brachytherapy is essential in the treatment of locally advanced cervical cancer. While Iridium-192 (Ir-192) is commonly used, its short half-life imposes logistical and financial constraints, particularly in low- and middle-income countries (LMICs). Cobalt-60 (Co-60), with a longer half-life and lower operational costs, is a viable alternative.
View Article and Find Full Text PDFRep Pract Oncol Radiother
August 2025
Department of Electroradiology, Poznan University of Medical Sciences, Poznan, Poland.
Background: To compare doses deposited to the liver during right breast radiotherapy with static and dynamic radiotherapy techniques. The second aim was to introduce the liver load index (LLI), a novel index developed to estimate radiation exposure to the liver prior to treatment selection.
Materials And Methods: We prepared radiotherapy treatment plans for ten patients with right breast cancer.
Brachytherapy
September 2025
Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, 10-6 Sakaemachi, Fukushima, Fukushima, 960-8516, Japan.
Purpose: This study presents the dose-based intra-preplan (DIP) method for intracavitary/interstitial brachytherapy (IC/ISBT) in cervical cancer, optimizing catheter configurations based on dose distribution. This study aimed to assess the DIP method's clinical feasibility and efficacy.
Methods And Materials: The DIP method incorporates the implant modeling function and the hybrid inverse planning optimization algorithm in Oncentra Brachy.
J Biomed Phys Eng
August 2025
Department of Medical Physics, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Background: Modern radiotherapy techniques can destroy tumors with less harm to surrounding normal tissues. Normal Tissue Complication Probability (NTCP) models are useful to evaluate treatment plans.
Objective: This study aimed to use the Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) program to evaluate dose-volume indicators and radiobiological parameters for complications of the rectum and bladder in prostate cancer patients undergoing pelvic radiotherapy.
ArXiv
August 2025
Department of Physics, The University of Texas at Arlington, Arlington, TX, United States.
Objective: This study aims to uncover the opaque decision-making process of an artificial intelligence (AI) agent for automatic treatment planning.
Approach: We examined a previously developed AI agent based on the Actor-Critic with Experience Replay (ACER) network, which automatically tunes treatment planning parameters (TPPs) for inverse planning in prostate cancer intensity modulated radiotherapy. We selected multiple checkpoint ACER agents from different stages of training and applied an explainable AI (EXAI) method to analyze the attribution from dose-volume histogram (DVH) inputs to TPP-tuning decisions.