scholarly journals Dynamics of high risk clinical target volume reduction during brachytherapy and impact on its coverage in patients with inoperable cervical cancer

Neoplasma ◽  
2018 ◽  
Vol 65 (03) ◽  
pp. 425-430
Author(s):  
M. POBIJAKOVA ◽  
D. SCEPANOVIC ◽  
M. PALUGA ◽  
M. FEKETE ◽  
J. MARDIAK
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Clinical Target Volume ◽  
Target Volume ◽  
Volume Reduction

Assessment of uterus position as a surrogate for high-risk clinical target volume with respect to the applicator position for multiple fractions of brachytherapy in cervical cancer

2014 ◽  
Vol 290 (6) ◽  
pp. 1201-1205 ◽  
Author(s):  
Rachel Cooper ◽  
Elizabeth Brearley ◽  
Pervin Hurmuz ◽  
Hima Bindu Musunuru ◽  
Carolyn Richardson ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Clinical Target Volume ◽  
Target Volume

scholarly journals Recommendations for high-risk clinical target volume definition with computed tomography for three-dimensional image-guided brachytherapy in cervical cancer patients

2016 ◽  
Vol 58 (3) ◽  
pp. 341-350 ◽  
Author(s):  
Tatsuya Ohno ◽  
Masaru Wakatsuki ◽  
Takafumi Toita ◽  
Yuko Kaneyasu ◽  
Ken Yoshida ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cervical Cancer ◽  
High Risk ◽  
Radiation Oncology ◽  
Clinical Target Volume ◽  
Guideline Development ◽  
Three Dimensional ◽  
Target Volume ◽  
Email Communication ◽  
Image Guided

Abstract Our purpose was to develop recommendations for contouring the computed tomography (CT)-based high-risk clinical target volume (CTVHR) for 3D image-guided brachytherapy (3D-IGBT) for cervical cancer. A 15-member Japanese Radiation Oncology Study Group (JROSG) committee with expertise in gynecological radiation oncology initiated guideline development for CT-based CTVHR (based on a comprehensive literature review as well as clinical experience) in July 2014. Extensive discussions occurred during four face-to-face meetings and frequent email communication until a consensus was reached. The CT-based CTVHR boundaries were defined by each anatomical plane (cranial–caudal, lateral, or anterior–posterior) with or without tumor progression beyond the uterine cervix at diagnosis. Since the availability of magnetic resonance imaging (MRI) with applicator insertion for 3D planning is currently limited, T2-weighted MRI obtained at diagnosis and just before brachytherapy without applicator insertion was used as a reference for accurately estimating the tumor size and topography. Furthermore, utilizing information from clinical examinations performed both at diagnosis and brachytherapy is strongly recommended. In conclusion, these recommendations will serve as a brachytherapy protocol to be used at institutions with limited availability of MRI for 3D treatment planning.

The value of tumor paramaters and dosimetry on prognostic nomograms for cervical cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e17026-e17026
Author(s):  
Shun Lu
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Clinical Features ◽  
Clinical Target Volume ◽  
Tumor Volume ◽  
External Beam Radiotherapy ◽  
Figo Stage ◽  
Target Volume ◽  
External Beam

e17026 Background: A range of prognostic factors had been reported to be associated with clinical outcome of cervical cancer patients. However, most of these parameters were measured before the start of treatment but without consideration of tumor response to RT. We believed that it might be possible to provide locally advanced cervical cancer patients an opportunity to modify and guide the treatment strategies in the midway of treatment based on the early response evaluation during RT. To establish effective prognostic nomograms using clinical features including tumor volume and size mesured by MRI before treatment and after the completion of external beam radiotherapy (EBRT), and detailed dosimetry of brachytherapy dose for high-risk clinical target volume (HRCTV)D90 and Low-risk Clinical Target Volume(LR-CTV)D90. Methods: The nomogram for local control (LC) was based on a retrospective study of 316 patients who underwent IMRT at our hospital from 2010 to 2015. The predictive accuracy and discriminative ability of our nomogram models were determined by concordance index and calibration curve, and were compared with the nomogram models combining clinical features with FIGO stage. The results were validated using bootstrap resampling and a cohort study of 141 patients. The same data cohort was used to predict the progress-free survival (PFS) of cervical cancer with 3:1 training cohort (N = 310) and validation cohort (N = 155). Results: The following factors were assembled into our prognostic survival nomogram models: Age, tumor volume and size (TV & TS) before treatment, TV and TS after the completion of external beam radiotherapy (EBRT), brachytherapy dose of high-risk clinical target volume (HRCTV)D90 and Low-risk Clinical Target Volume(LR-CTV)D90. The calibration curves showed good agreement between nomogram-predicted and actual survival. Our nomogram models for LC and PFS, provided better results than the nomogram models combining clinical features with FIGO stage. Results were further confirmed in the validation set. Conclusions: Clinical features including tumor size and volume mesured before treatment and after EBRT, as well as detailed dosimetry of brachytherapy dose are able to improve the performance of prognostic nomograms for patients with cervical cancer.

scholarly journals CT based automatic clinical target volume delineation using a dense-fully connected convolution network for cervical Cancer radiation therapy

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhongjian Ju ◽  
Wen Guo ◽  
Shanshan Gu ◽  
Jin Zhou ◽  
Wei Yang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Patients ◽  
Clinical Target Volume ◽  
Target Volume ◽  
Ct Images ◽  
Clinical Samples ◽  
Model Parameters ◽  
Convolutional Network ◽  
Volume Delineation ◽  
Fully Connected

Abstract Background It is very important to accurately delineate the CTV on the patient’s three-dimensional CT image in the radiotherapy process. Limited to the scarcity of clinical samples and the difficulty of automatic delineation, the research of automatic delineation of cervical cancer CTV based on CT images for new patients is slow. This study aimed to assess the value of Dense-Fully Connected Convolution Network (Dense V-Net) in predicting Clinical Target Volume (CTV) pre-delineation in cervical cancer patients for radiotherapy. Methods In this study, we used Dense V-Net, a dense and fully connected convolutional network with suitable feature learning in small samples to automatically pre-delineate the CTV of cervical cancer patients based on computed tomography (CT) images and then we assessed the outcome. The CT data of 133 patients with stage IB and IIA postoperative cervical cancer with a comparable delineation scope was enrolled in this study. One hundred and thirteen patients were randomly designated as the training set to adjust the model parameters. Twenty cases were used as the test set to assess the network performance. The 8 most representative parameters were also used to assess the pre-sketching accuracy from 3 aspects: sketching similarity, sketching offset, and sketching volume difference. Results The results presented that the DSC, DC/mm, HD/cm, MAD/mm, ∆V, SI, IncI and JD of CTV were 0.82 ± 0.03, 4.28 ± 2.35, 1.86 ± 0.48, 2.52 ± 0.40, 0.09 ± 0.05, 0.84 ± 0.04, 0.80 ± 0.05, and 0.30 ± 0.04, respectively, and the results were greater than those with a single network. Conclusions Dense V-Net can correctly predict CTV pre-delineation of cervical cancer patients and can be applied in clinical practice after completing simple modifications.

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