scholarly journals The clinical target volume in lung, head-and-neck, and esophageal cancer: Lessons from pathological measurement and recurrence analysis

2017 ◽  
Vol 3 ◽  
pp. 1-8 ◽  
Author(s):  
Rudi Apolle ◽  
Maximilian Rehm ◽  
Thomas Bortfeld ◽  
Michael Baumann ◽  
Esther G.C. Troost
Medicina ◽  
2020 ◽  
Vol 57 (1) ◽  
pp. 6
Author(s):  
Camil Ciprian Mireştean ◽  
Anda Crişan ◽  
Călin Buzea ◽  
Roxana Irina Iancu ◽  
DragoşPetru Teodor Iancu

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).


2011 ◽  
Vol 38 (6Part8) ◽  
pp. 3465-3465
Author(s):  
J Yang ◽  
R Williamson ◽  
A Garden ◽  
D Rosenthal ◽  
Y Zhang ◽  
...  

2021 ◽  
Vol 20 ◽  
pp. 153303382110342
Author(s):  
Ruifen Cao ◽  
Xi Pei ◽  
Ning Ge ◽  
Chunhou Zheng

Radiotherapy plays an important role in controlling the local recurrence of esophageal cancer after radical surgery. Segmentation of the clinical target volume is a key step in radiotherapy treatment planning, but it is time-consuming and operator-dependent. This paper introduces a deep dilated convolutional U-network to achieve fast and accurate clinical target volume auto-segmentation of esophageal cancer after radical surgery. The deep dilated convolutional U-network, which integrates the advantages of dilated convolution and the U-network, is an end-to-end architecture that enables rapid training and testing. A dilated convolution module for extracting multiscale context features containing the original information on fine texture and boundaries is integrated into the U-network architecture to avoid information loss due to down-sampling and improve the segmentation accuracy. In addition, batch normalization is added to the deep dilated convolutional U-network for fast and stable convergence. In the present study, the training and validation loss tended to be stable after 40 training epochs. This deep dilated convolutional U-network model was able to segment the clinical target volume with an overall mean Dice similarity coefficient of 86.7% and a respective 95% Hausdorff distance of 37.4 mm, indicating reasonable volume overlap of the auto-segmented and manual contours. The mean Cohen kappa coefficient was 0.863, indicating that the deep dilated convolutional U-network was robust. Comparisons with the U-network and attention U-network showed that the overall performance of the deep dilated convolutional U-network was best for the Dice similarity coefficient, 95% Hausdorff distance, and Cohen kappa coefficient. The test time for segmentation of the clinical target volume was approximately 25 seconds per patient. This deep dilated convolutional U-network could be applied in the clinical setting to save time in delineation and improve the consistency of contouring.


2021 ◽  
Vol 34 (Supplement_1) ◽  
Author(s):  
Tahseena Tahmeed ◽  
Anil Tibdewal ◽  
Sarbani Ghosh-Laskar ◽  
Naveen Mummudi ◽  
Vanita Noronha ◽  
...  

Abstract   Esophageal cancer is a locally aggressive malignancy with dismal overall survival (OS) rates. Approximately 50–60% of patients fail loco-regionally after definitive chemoradiation (CTRT). There is a lack of consensus regarding clinical target volume (CTV) margins. Improved diagnostic investigations and patterns of failure (POF) data, suggested scope of reduced CTV margins. In this retrospective study, we evaluated the POF (defined as first site of failure) and the adequacy of CTV margins. Methods All patients treated with CTRT between Jan 2013 to Dec 2017 were included. CTV margin was given as 3–5 cm cranio-caudal and 1–1.5 cm radial from gross tumor volume (GTV). Patients were treated either with combined technique (anterior–posterior followed by conformal) or with volumetric arc radiotherapy to a dose of 60-63Gy in 30–35 fractions. PET-CT/CT thorax and upper GI-endoscopy were performed at regular intervals. Loco-regional failure (LRF) was defined as recurrence at local site or regional nodes respectively and classified as infield, marginal and out-field. CT was co-registered with planning CT to document these failures. Results 158 patients were eligible. Twenty-one patients were excluded as they either progressed or did not attend the first follow-up. Median age was 57 years, >90% had squamous histology, and most common subsite was upper third. Median follow-up was 45.8 months, 53 patients (41.7%) had progression. Local recurrence (LR) was seen in 37 (69.8%), followed by regional in 25 (47.1%) and distant in 21 patients (39.6%). All LR were within the GTV. Of regional failures, 50% were within GTV and 50% were outside the radiotherapy portals. This suggest that 3 cm cranio-caudal CTV margin was adequate. Conclusion Our study demonstrated that loco-regional recurrence was the most common pattern of failure after definitive CTRT. As majority of loco-regional failures were within the GTV, hence, 3 cm cranio-caudal CTV margins appear to be adequate enough for control of microscopic disease. Further prospective studies are needed to validate the use of 3 cm CTV margins in definitive CTRT for esophageal cancer.


2001 ◽  
Vol 87 (3) ◽  
pp. 152-161 ◽  
Author(s):  
Giuseppe Sanguineti ◽  
Franca Foppiano ◽  
Michela Marcenaro ◽  
Federico Roncallo ◽  
Renzo Corvò ◽  
...  

2004 ◽  
Vol 59 (5) ◽  
pp. 1301-1311 ◽  
Author(s):  
Ian Poon ◽  
Nancy Fischbein ◽  
Nancy Lee ◽  
Pamela Akazawa ◽  
Ping Xia ◽  
...  

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