Venezia applicator with oblique needles improves clinical target volume coverage in distal parametrial tumor residue compared to parallel needles only

The combination of immune checkpoint inhibitors and definitive radiotherapy is investigated for the multimodal treatment of cisplatin non-eligible locally advanced head and neck cancers (HNC). In the case of recurrent and metastatic HNC, immunotherapy has shown benefit over the EXTREME protocol, being already considered the standard treatment. One of the biggest challenges of multimodal treatment is to establish the optimal therapy sequence so that the synergistic effect is maximal. Thus, superior results were obtained for the administration of anti-CTLA4 immunotherapy followed by hypofractionated radiotherapy, but the anti-PD-L1 therapy demonstrates the maximum potential of radio-sensitization of the tumor in case of concurrent administration. The synergistic effect of radiotherapy–immunotherapy (RT–IT) has been demonstrated in clinical practice, with an overall response rate of about 18% for HNC. Given the demonstrated potential of radiotherapy to activate the immune system through already known mechanisms, it is necessary to identify biomarkers that direct the “nonresponders” of immunotherapy towards a synergistic RT–IT stimulation strategy. Stimulation of the immune system by irradiation can convert “nonresponder” to “responder”. With the development of modern techniques, re-irradiation is becoming an increasingly common option for patients who have previously been treated with higher doses of radiation. In this context, radiotherapy in combination with immunotherapy, both in the advanced local stage and in recurrent/metastatic of HNC radiotherapy, could evolve from the “first level” of knowledge (i.e., ballistic precision, dose conformity and homogeneity) to “level two” of “biological dose painting” (in which the concept of tumor heterogeneity and radio-resistance supports the need for doses escalation based on biological criteria), and finally to the “third level“ ofthe new concept of “immunological dose painting”. The peculiarity of this concept is that the radiotherapy target volumes and tumoricidal dose can be completely reevaluated, taking into account the immune-modulatory effect of irradiation. In this case, the tumor target volume can include even the tumor microenvironment or a partial volume of the primary tumor or metastasis, not all the gross and microscopic disease. Tumoricidal biologically equivalent dose (BED) may be completely different from the currently estimated values, radiotherapy treating the tumor in this case indirectly by boosting the immune response. Thus, the clinical target volume (CTV) can be replaced with a new immunological-clinical target volume (ICTV) for patients who benefit from the RT–IT association (Image 1).

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Automated Clinical Target Volume Delineation for Non-Small Cell Lung Cancer Patients Using Deep 3D Networks

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2020.07.827 ◽

2020 ◽

Vol 108 (3) ◽

pp. e347

Author(s):

Y. Xie ◽

K. Kang ◽

Y. Wang ◽

F.K. Keane ◽

M.J. Khandekar ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Patients ◽

Clinical Target Volume ◽

Target Volume ◽

Small Cell ◽

Small Cell Lung ◽

Target Volume Delineation ◽

Lung Cancer Patients ◽

Volume Delineation ◽

3D Networks

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Clinical target volume design of postoperative intensity-modulated radiotherapy for major salivary gland tumours according to surgical principles: an innovative method

Journal of Cancer Research and Clinical Oncology ◽

10.1007/s00432-021-03646-y ◽

2021 ◽

Author(s):

Shaowen Lyu ◽

Zheng Wu ◽

Dehuan Xie ◽

Zhiqing Long ◽

Rui Zhong ◽

...

Keyword(s):

Salivary Gland ◽

Clinical Target Volume ◽

Intensity Modulated Radiotherapy ◽

Target Volume ◽

Major Salivary Gland ◽

Salivary Gland Tumours ◽

Intensity Modulated ◽

Innovative Method

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

BMC Cancer ◽

10.1186/s12885-020-07595-6 ◽

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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Determining optimal clinical target volume margins in high-grade glioma based on microscopic tumor extension and magnetic resonance imaging

Radiation Oncology ◽

10.1186/s13014-021-01819-0 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Shulun Nie ◽

Yufang Zhu ◽

Jia Yang ◽

Tao Xin ◽

Song Xue ◽

...

Keyword(s):

Dna Methyltransferase ◽

Goodness Of Fit ◽

Clinical Target Volume ◽

Characteristic Curve ◽

Area Under The Curve ◽

High Grade Glioma ◽

Target Volume ◽

Tumor Location ◽

High Grade ◽

Clinical Trial Registry

Abstract Introduction In this study, we performed a consecutive macropathologic analysis to assess microscopic extension (ME) in high-grade glioma (HGG) to determine appropriate clinical target volume (CTV) margins for radiotherapy. Materials and methods The study included HGG patients with tumors located in non-functional areas, and supratotal resection was performed. The ME distance from the edge of the tumor to the microscopic tumor cells surrounding brain tissue was measured. Associations between the extent of ME and clinicopathological characteristics were evaluated by multivariate linear regression (MVLR) analysis. An ME predictive model was developed based on the MVLR model. Results Between June 2017 and July 2019, 652 pathologic slides obtained from 30 HGG patients were analyzed. The mean ME distance was 1.70 cm (range, 0.63 to 2.87 cm). The MVLR analysis identified that pathologic grade, subventricular zone (SVZ) contact and O6-methylguanine-DNA methyltransferase (MGMT) methylation, isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status were independent variables predicting ME (all P < 0.05). A multivariable prediction model was developed as follows: YME = 0.672 + 0.513XGrade + 0.380XSVZ + 0.439XMGMT + 0.320XIDH + 0.333X1p/19q. The R-square value of goodness of fit was 0.780. The receiver operating characteristic curve proved that the area under the curve was 0.964 (P < 0.001). Conclusion ME was heterogeneously distributed across different grades of gliomas according to the tumor location and molecular marker status, which indicated that CTV delineation should be individualized. The model could predict the ME of HGG, which may help clinicians determine the CTV for individual patients. Trial registration The trial was registered with Chinese Clinical Trial Registry (ChiCTR2100046106). Registered 4 May 2021-Retrospectively registered.

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