Investigation of the distribution of inguinal lymph nodes and delineation of the inguinal clinical target volume using 18F-FDG PET/CT.

OBJECTIVE: Radiotherapy is a crucial treatment modality for pelvic cancers, but uncertainties persist in defining the clinical target volume (CTV) for the inguinal lymphatic drainage region. Suboptimal CTV delineation may compromise treatment efficacy and result in subpar disease control. This study aimed to investigate and map the distribution of lymph node metastases (LNM) in the groin area to facilitate an improved and detailed CTV definition using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). METHODS: Inguinal LNM in patients with biopsy-proven pelvic malignancies were identified using 18F-FDG PET/CT scan. The longitudinally nearest axial plane was determined based on six typical bony landmarks, and the axial direction relative to the femoral artery of LNM was recorded. The distances from the LNM to the nearest edge of the femoral artery were measured on the axial plane. An optimal margin to cover 95% of LNM was estimated to develop contouring recommendations. RESULTS: In this study, 500 positive LNM were identified by 18F-FDG PET/CT among 185 patients with primary pelvic malignancies. Relative to the femoral artery, lymph nodes were distributed laterally (10:00-11:00, n = 35), anteriorly (12:00-1:00, n = 213), and medially (2:00-4: 00, n = 252). For CTV delineation, the recommended distances from the femoral artery on the SFH were lateral 19 mm, anterior 19 mm, and medial 25 mm; on the SGT were lateral 26 mm, anterior 20 mm, and medial 25 mm; on the SPS were lateral 28 mm, anterior 29 mm, and medial 26 mm; on the IPS were anterior 29 mm and medial 28 mm; on the IIT were anterior 27 mm and medial 27 mm; on the ILT were anterior 25 mm and medial 23 mm. Use interpolation to contour the area between six axial slices, including any radiographically suspicious LNM. CONCLUSIONS: Using 18F-FDG PET/CT, we investigated the distribution pattern of inguinal LNM and propose a more comprehensive guideline for inguinal CTV delineation.

Clinical evaluation of the convolutional neural network­based automatic delineation tool in determining the clinical target volume and organs at risk in rectal cancer radiotherapy.

Delineating the clinical target volume (CTV) and organs at risk (OARs) is crucial in rectal cancer radiotherapy. However, the accuracy of manual delineation (MD) is variable and the process is time consuming. Automatic delineation (AD) may be a solution to produce quicker and more accurate contours. In the present study, a convolutional neural network (CNN)-based AD tool was clinically evaluated to analyze its accuracy and efficiency in rectal cancer. CT images were collected from 148 supine patients in whom tumor stage and type of surgery were not differentiated. The rectal cancer contours consisted of CTV and OARs, where the OARs included the bladder, left and right femoral head, left and right kidney, spinal cord and bowel bag. The MD contours reviewed and modified together by a senior radiation oncologist committee were set as the reference values. The Dice similarity coefficient (DSC), Jaccard coefficient (JAC) and Hausdorff distance (HD) were used to evaluate the AD accuracy. The correlation between CT slice number and AD accuracy was analyzed, and the AD accuracy for different contour numbers was compared. The time recorded in the present study included the MD time, AD time for different CT slice and contour numbers and the editing time for AD contours. The Pearson correlation coefficient, paired-sample t-test and unpaired-sample t-test were used for statistical analyses. The results of the present study indicated that the DSC, JAC and HD for CTV using AD were 0.80±0.06, 0.67±0.08 and 6.96±2.45 mm, respectively. Among the OARs, the highest DSC and JAC using AD were found for the right and left kidney, with 0.91±0.06 and 0.93±0.04, and 0.84±0.09 and 0.88±0.07, respectively, and HD was lowest for the spinal cord with 2.26±0.82 mm. The lowest accuracy was found for the bowel bag. The more CT slice numbers, the higher the accuracy of the spinal cord analysis. However, the contour number had no effect on AD accuracy. To obtain qualified contours, the AD time plus editing time was 662.97±195.57 sec, while the MD time was 3294.29±824.70 sec. In conclusion, the results of the present study indicate that AD can significantly improve efficiency and a higher number of CT slices and contours can reduce AD efficiency. The AD tool provides acceptable CTV and OARs for rectal cancer and improves efficiency for delineation.

Clinical target volume design and dose in carbon-ion radiation therapy for sinonasal mucosal melanoma.

BACKGROUND AND PURPOSE: No guidelines exist for the clinical target volume (CTV) and radiotherapy dose in sinonasal mucosal melanoma (SNMM). Thus, we aimed to determine the carbon-ion radiotherapy (CIRT) CTV and dose for SNMM. MATERIALS AND METHODS: In total, 135 patients with SNMM who received CIRT were reviewed. The relative biological effectiveness-weighted dose was 57.6 or 64 Gy in 16 fractions. CTV was classified into small CTV, which included the gross tumor and visible melanosis with a certain margin, and extended CTV, which included the tumor site and adjacent anatomical structures. Local recurrence (LR) patterns were pattern I, II, and III, defined as recurrence over the gross tumor, visible melanosis and subclinical area, which would be covered if extended CTV was applied, and outside the extended CTV, respectively. RESULTS: The 5-year LR rate was 35.3 %. The prescribed dose was not a significant risk factor for pattern I LR; however, 57.6 Gy for a large tumor was insufficient for local control. Using an extended CTV was significantly associated with a lower risk of pattern II LR, and these recurrences did not occur in regions that received > 40 Gy. The 5-year pattern III LR rate was 6.4 %. CONCLUSION: Utilizing an extended CTV in CIRT for SNMM is appropriate even for small tumors. Using a smaller CTV after an extended CTV of at least 40 Gy is recommended to reduce adverse events. Although the optimal dose for gross tumors remains unclear, the latest technology with 64 Gy showed good outcomes.

Failure patterns and individualized treatment plans of reirradiation for inoperable locally recurrent nasopharyngeal carcinoma.

Re-irradiation with intensity-modulated radiotherapy (IMRT) remains the primary treatment modality for inoperable locally recurrent nasopharyngeal carcinoma (NPC). However, the rate of radiation-related late adverse effects is often substantially high. Therefore, we aimed to explore failure patterns and individualized treatment plans of re-irradiation for inoperable locally recurrent NPC. Ninety-seven patients who underwent IMRT were retrospectively analyzed. Sixty-two patients had clinical target volume of recurrence (rCTV) delineated, and thirty-five patients had only gross tumor volume of recurrence (rGTV) delineated. Twenty-nine patients developed second local failures after re-irradiation with IMRT (28 cases available). Among those patients, 64.3% (18/28) of patients and 35.7% (10/28) developed in-field or out-field, respectively. No statistical correlation was observed between target volume (rGTV or rCTV) and the local recurrence rate, local failure patterns, grade ≥ 3 toxicity, and survival. Multivariate analysis showed that recurrent T (rT) stage (HR 2.62, P = 0.019) and rGTV volume (HR 1.73, P = 0.037) were independent prognostic factors for overall survival (OS). Risk stratification based on rT stage and rGTV volume revealed that low risk group had a longer 3-year OS rate (66.7% vs. 23.4%), lower total grade ≥ 3 toxicity (P = 0.004), and lower re-radiation associated mortality rates (HR 0.45, P = 0.03) than high risk group. This study demonstrates that the delineation of rCTV may not be beneficial for re-irradiation using IMRT in locally recurrent NPC. Patients with low risk were most suitable for re-irradiation, with maximizing local salvage and minimizing radiation-related toxicities. More precise and individualized plans of re-irradiation are warranted.

Measurement of the distance between tumor micro-foci and gross tumor in rectal cancer pathological specimens: implication on margin distance of clinical target volume treated with high-dose radiotherapy for rectal cancer.

PURPOSE: To measure the micro-foci distance away from gross tumor and to provide reference to create the clinical target volume (CTV) margin for boost radiotherapy in rectal adenocarcinoma. METHODS: Twenty-eight rectal cancer surgical specimens of only total mesorectal excision were collected. The pathological specimens were retrospectively measured, and the nearest distance between the tumor micro-foci and gross tumor was microscopically measured. The "in vivo-in vitro" retraction factor was calculated as the ratio of the deepest thickness laterally and the vertical height superior/inferiorly of the rectal tumor measured in MRI and those measured in immediate pathological specimens. The retraction factor during pathological specimen processing was calculated as the distance ratio before and after dehydration in the lateral, superior, and inferior sides by the "knot marking method." The distances of tumor micro-foci were individually corrected with these two retraction factors. RESULTS: The mean "in vivo-in vitro" tumor retraction factors were 0.913 peripherally and 0.920 superior/inferiorly. The mean tumor specimen processing retraction factors were 0.804 peripherally, 0.815 inferiorly, and 0.789 superiorly. Of 28 patients, 14 cases (50.0%) had 24 lateral micro-foci, 8 cases (28.6%) had 13 inferior micro-foci, and 7 cases (25.0%) had 19 superior micro-foci. The 95th percentiles of the micro-foci distance for 28 patients were 6.44 mm (peripheral), 5.54 mm (inferior), and 5.42 mm (superior) after retraction correction. CONCLUSION: The micro-foci distances of 95% of rectal adenocarcinoma patients examined were within 6.44 mm peripherally, 5.54 mm inferiorly, and 5.42 mm superiorly. These findings provide reference to set the boost radiotherapy CTV margin for rectal cancer.

Landmark-based auto-contouring of clinical target volumes for radiotherapy of nasopharyngeal cancer.

BACKGROUND: The delineation of clinical target volumes (CTVs) for radiotherapy for nasopharyngeal cancer is complex and varies based on the location and extent of disease. PURPOSE: The current study aimed to develop an auto-contouring solution following one protocol guidelines (NRG-HN001) that can be adjusted to meet other guidelines, such as RTOG-0225 and the 2018 International guidelines. METHODS: The study used 2-channel 3-dimensional U-Net and nnU-Net framework to auto-contour 27 normal structures in the head and neck (H&N) region that are used to define CTVs in the protocol. To define the CTV-Expansion (CTV1 and CTV2) and CTV-Overall (the outer envelope of all the CTV contours), we used adjustable morphological geometric landmarks and mimicked physician interpretation of the protocol rules by partially or fully including select anatomic structures. The results were evaluated quantitatively using the dice similarity coefficient (DSC) and mean surface distance (MSD) and qualitatively by independent reviews by two H&N radiation oncologists. RESULTS: The auto-contouring tool showed high accuracy for nasopharyngeal CTVs. Comparison between auto-contours and clinical contours for 19 patients with cancers of various stages showed a DSC of 0.94 ± 0.02 and MSD of 0.4 ± 0.4 mm for CTV-Expansion and a DSC of 0.83 ± 0.02 and MSD of 2.4 ± 0.5 mm for CTV-Overall. Upon independent review, two H&N physicians found the auto-contours to be usable without edits in 85% and 75% of cases. In 15% of cases, minor edits were required by both physicians. Thus, one physician rated 100% of the auto-contours as usable (use as is, or after minor edits), while the other physician rated 90% as usable. The second physician required major edits in 10% of cases. CONCLUSIONS: The study demonstrates the ability of an auto-contouring tool to reliably delineate nasopharyngeal CTVs based on protocol guidelines. The tool was found to be clinically acceptable by two H&N radiation oncology physicians in at least 90% of the cases.

Localized fine-tuning and clinical evaluation of deep-learning based auto-segmentation (DLAS) model for clinical target volume (CTV) and organs-at-risk (OAR) in rectal cancer radiotherapy.

BACKGROUND AND PURPOSE: Various deep learning auto-segmentation (DLAS) models have been proposed, some of which have been commercialized. However, the issue of performance degradation is notable when pretrained models are deployed in the clinic. This study aims to enhance precision of a popular commercial DLAS product in rectal cancer radiotherapy by localized fine-tuning, addressing challenges in practicality and generalizability in real-world clinical settings. MATERIALS AND METHODS: A total of 120 Stage II/III mid-low rectal cancer patients were retrospectively enrolled and divided into three datasets: training (n = 60), external validation (ExVal, n = 30), and generalizability evaluation (GenEva, n = 30) datasets respectively. The patients in the training and ExVal dataset were acquired on the same CT simulator, while those in GenEva were on a different CT simulator. The commercial DLAS software was first localized fine-tuned (LFT) for clinical target volume (CTV) and organs-at-risk (OAR) using the training data, and then validated on ExVal and GenEva respectively. Performance evaluation involved comparing the LFT model with the vendor-provided pretrained model (VPM) against ground truth contours, using metrics like Dice similarity coefficient (DSC), 95th Hausdorff distance (95HD), sensitivity and specificity. RESULTS: LFT significantly improved CTV delineation accuracy (p < 0.05) with LFT outperforming VPM in target volume, DSC, 95HD and specificity. Both models exhibited adequate accuracy for bladder and femoral heads, and LFT demonstrated significant enhancement in segmenting the more complex small intestine. We did not identify performance degradation when LFT and VPM models were applied in the GenEva dataset. CONCLUSIONS: The necessity and potential benefits of LFT DLAS towards institution-specific model adaption is underscored. The commercial DLAS software exhibits superior accuracy once localized fine-tuned, and is highly robust to imaging equipment changes.

Uncertainty estimation using a 3D probabilistic U-Net for segmentation with small radiotherapy clinical trial datasets.

BACKGROUND AND OBJECTIVES: Bio-medical image segmentation models typically attempt to predict one segmentation that resembles a ground-truth structure as closely as possible. However, as medical images are not perfect representations of anatomy, obtaining this ground truth is not possible. A surrogate commonly used is to have multiple expert observers define the same structure for a dataset. When multiple observers define the same structure on the same image there can be significant differences depending on the structure, image quality/modality and the region being defined. It is often desirable to estimate this type of aleatoric uncertainty in a segmentation model to help understand the region in which the true structure is likely to be positioned. Furthermore, obtaining these datasets is resource intensive so training such models using limited data may be required. With a small dataset size, differing patient anatomy is likely not well represented causing epistemic uncertainty which should also be estimated so it can be determined for which cases the model is effective or not. METHODS: We use a 3D probabilistic U-Net to train a model from which several segmentations can be sampled to estimate the range of uncertainty seen between multiple observers. To ensure that regions where observers disagree most are emphasised in model training, we expand the Generalised Evidence Lower Bound (ELBO) with a Constrained Optimisation (GECO) loss function with an additional contour loss term to give attention to this region. Ensemble and Monte-Carlo dropout (MCDO) uncertainty quantification methods are used during inference to estimate model confidence on an unseen case. We apply our methodology to two radiotherapy clinical trial datasets, a gastric cancer trial (TOPGEAR, TROG 08.08) and a post-prostatectomy prostate cancer trial (RAVES, TROG 08.03). Each dataset contains only 10 cases each for model development to segment the clinical target volume (CTV) which was defined by multiple observers on each case. An additional 50 cases are available as a hold-out dataset for each trial which had only one observer define the CTV structure on each case. Up to 50 samples were generated using the probabilistic model for each case in the hold-out dataset. To assess performance, each manually defined structure was matched to the closest matching sampled segmentation based on commonly used metrics. RESULTS: The TOPGEAR CTV model achieved a Dice Similarity Coefficient (DSC) and Surface DSC (sDSC) of 0.7 and 0.43 respectively with the RAVES model achieving 0.75 and 0.71 respectively. Segmentation quality across cases in the hold-out datasets was variable however both the ensemble and MCDO uncertainty estimation approaches were able to accurately estimate model confidence with a p-value < 0.001 for both TOPGEAR and RAVES when comparing the DSC using the Pearson correlation coefficient. CONCLUSIONS: We demonstrated that training auto-segmentation models which can estimate aleatoric and epistemic uncertainty using limited datasets is possible. Having the model estimate prediction confidence is important to understand for which unseen cases a model is likely to be useful.

Integrating muscle fiber orientation from visible human data into radiotherapy target volumes.

Objective.A major challenge in treatment of tumors near skeletal muscle is defining the target volume for suspected tumor invasion into the muscle. This study develops a framework that generates radiation target volumes with muscle fiber orientation directly integrated into their definition. The framework is applied to nineteen sacral tumor patients with suspected infiltration into surrounding muscles.Approach.To compensate for the poor soft-tissue contrast of CT images, muscle fiber orientation is derived from cryo-images of two cadavers from the human visible project (VHP). The approach consists of (a) detecting image gradients in the cadaver images representative of muscle fibers, (b) mapping this information onto the patient image, and (c) embedding the muscle fiber orientation into an expansion method to generate patient-specific clinical target volumes (CTV). The validation tested the consistency of image gradient orientation across VHP subjects for the piriformis, gluteus maximus, paraspinal, gluteus medius, and gluteus minimus muscles. The model robustness was analyzed by comparing CTVs generated using different VHP subjects. The difference in shape between the new CTVs and standard CTV was analyzed for clinical impact.Main results.Good agreement was found between the image gradient orientation across VHP subjects, as the voxel-wise median cosine similarity was at least 0.86 (for the gluteus minimus) and up to 0.98 for the piriformis. The volume and surface similarity between the CTVs generating from different VHP subjects was on average at least 0.95 and 5.13 mm for the Dice Similarity Coefficient and the Hausdorff 95% Percentile Index, showing excellent robustness. Finally, compared to the standard CTV with different margins in muscle and non-muscle tissue, the new CTV margins are reduced in muscle tissue depending on the chosen clinical margins.Significance.This study implements a method to integrate muscle fiber orientation into the target volume without the need for additional imaging.

Comparison of different target volume delineation strategies based on recurrence patterns in adjuvant radiotherapy for glioblastoma.

Background: Radiation Therapy Oncology Group (RTOG) and the European Organization for Research and Treatment of Cancer (EORTC) recommendations are commonly used guidelines for adjuvant radiotherapy in glioblastoma. In our institutional protocol, we delineate T2-FLAIR alterations as gross target volume (GTV) with reduced clinical target volume (CTV) margins. We aimed to present our oncologic outcomes and compare the recurrence patterns and planning parameters with EORTC and RTOG delineation strategies. Methods: Eighty-one patients who received CRT between 2014 and 2021 were evaluated retrospectively. EORTC and RTOG delineations performed on the simulation computed tomography and recurrence patterns and planning parameters were compared between delineation strategies. Statistical Package for the Social Sciences (SPSS) version 23.0 (IBM, Armonk, NY, USA) was utilized for statistical analyses. Results: Median overall survival and progression-free survival were 21 months and 11 months, respectively. At a median 18 month follow-up, of the 48 patients for whom recurrence pattern analysis was performed, recurrence was encompassed by only our institutional protocol's CTV in 13 (27%) of them. For the remaining 35 (73%) patients, recurrence was encompassed by all separate CTVs. In addition to the 100% rate of in-field recurrence, the smallest CTV and lower OAR doses were obtained by our protocol. Conclusions: The current study provides promising results for including the T2-FLAIR alterations to the GTV with smaller CTV margins with impressive survival outcomes without any marginal recurrence. The fact that our protocol did not result in larger irradiated brain volume is further encouraging in terms of toxicity.

Microinvasion in hepatocellular carcinoma: predictive factor and application for definition of clinical target volume for radiotherapy.

BACKGROUND: To investigate the correlation between microinvasion and various features of hepatocellular carcinoma (HCC), and to clarify the microinvasion distance from visible HCC lesions to subclinical lesions, so as to provide clinical basis for the expandable boundary of clinical target volume (CTV) from gross tumor volume (GTV) in the radiotherapy of HCC. METHODS: HCC patients underwent hepatectomy of liver cancer in our hospital between July 2019 and November 2021 were enrolled. Data on various features and tumor microinvasion distance were collected. The distribution characteristics of microinvasion distance were analyzed to investigate its potential correlation with various features. Tumor size compared between radiographic and pathologic samples was analyzed to clarify the application of pathologic microinvasion to identify subclinical lesions of radiographic imaging. RESULTS: The average microinvasion distance was 0.6 mm, with 95% patients exhibiting microinvasion distance less than 3.0 mm, and the maximum microinvasion distance was 4.0 mm. A significant correlation was found between microinvasion and liver cirrhosis (P = 0.036), serum albumin level (P = 0.049). Multivariate logistic regression analysis revealed that HCC patients with cirrhosis had a significantly lower risk of microinvasion (OR = 0.09, 95%CI = 0.02 ~ 0.50, P = 0.006). Tumor size was overestimated by 1.6 mm (95%CI=-12.8 ~ 16.0 mm) on radiographic size compared to pathologic size, with a mean %Δsize of 2.96% (95%CI=-0.57%~6.50%). The %Δsize ranged from - 29.03% to 34.78%. CONCLUSIONS: CTV expanding by 5.4 mm from radiographic GTV could include all pathologic microinvasive lesions in the radiotherapy of HCC. Liver cirrhosis was correlated with microinvasion and were independent predictive factor of microinvasion in HCC.

Long-term follow-up of protective effects on salivary and swallowing structures and improvement of late xerostomia and dysphagia by level IIb optimisation in clinical target volume of nasopharyngeal carcinoma.

BACKGROUND: This study aimed to assess the long-term effect of level IIb clinical target volume (CTV) optimisation on survival, xerostomia, and dysphagia in patients with nasopharyngeal carcinoma (NPC). METHODS: Clinical data of 415 patients with NPC treated with intensity-modulated radiotherapy between December 2014 and October 2018 were retrospectively analysed. The patients were categorised into modified and comparison groups. Late xerostomia and dysphagia were evaluated using Radiation Therapy Oncology Group/European Organisation for Research and Treatment of Cancer scoring. Survival analysis was performed using the Kaplan-Meier method. Differences in late toxicity and dose parameters between both groups were compared. Prognostic factors for survival and late toxicity were assessed using regression analyses. RESULTS: Patients in the modified group developed late xerostomia and dysphagia less frequently than those in the comparison group did (P < 0.001). The mean dose (Dmean) and V26 of parotid glands; Dmean and V39 of submandibular glands; and Dmean of sublingual glands, oral cavity, larynx, and superior, middle, and lower pharyngeal constrictor muscles were lower in the modified group than those in the comparison group (all P < 0.001). Both groups had no significant differences in overall, local recurrence-free, distant metastasis-free, or progression-free survival. The Dmean of the parotid and sublingual glands was a risk factor for xerostomia. The Dmean of the parotid and sublingual glands and middle pharyngeal constrictor muscle was a risk factor for dysphagia. CONCLUSIONS: Level IIb optimisation in NPC patients who meet certain criteria specially the exclusion of positive retropharyngeal nodes treated with intensity-modulated radiotherapy has the potential to better protect the salivary and swallowing structures, decreasing the development of late radiation-induced xerostomia and dysphagia while maintaining long-term survival.

Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer.

OBJECTIVES: Accurate segmentation of the clinical target volume (CTV) of CBCT images can observe the changes of CTV during patients' radiotherapy, and lay a foundation for the subsequent implementation of adaptive radiotherapy (ART). However, segmentation is challenging due to the poor quality of CBCT images and difficulty in obtaining target volumes. An uncertainty estimation- and attention-based semi-supervised model called residual convolutional block attention-uncertainty aware mean teacher (RCBA-UAMT) was proposed to delineate the CTV in cone-beam computed tomography (CBCT) images of breast cancer automatically. METHODS: A total of 60 patients who undergone radiotherapy after breast-conserving surgery were enrolled in this study, which involved 60 planning CTs and 380 CBCTs. RCBA-UAMT was proposed by integrating residual and attention modules in the backbone network 3D UNet. The attention module can adjust channel and spatial weights of the extracted image features. The proposed design can train the model and segment CBCT images with a small amount of labeled data (5%, 10%, and 20%) and a large amount of unlabeled data. Four types of evaluation metrics, namely, dice similarity coefficient (DSC), Jaccard, average surface distance (ASD), and 95% Hausdorff distance (95HD), are used to assess the model segmentation performance quantitatively. RESULTS: The proposed method achieved average DSC, Jaccard, 95HD, and ASD of 82%, 70%, 8.93, and 1.49 mm for CTV delineation on CBCT images of breast cancer, respectively. Compared with the three classical methods of mean teacher, uncertainty-aware mean-teacher and uncertainty rectified pyramid consistency, DSC and Jaccard increased by 7.89-9.33% and 14.75-16.67%, respectively, while 95HD and ASD decreased by 33.16-67.81% and 36.05-75.57%, respectively. The comparative experiment results of the labeled data with different proportions (5%, 10% and 20%) showed significant differences in the DSC, Jaccard, and 95HD evaluation indexes in the labeled data with 5% versus 10% and 5% versus 20%. Moreover, no significant differences were observed in the labeled data with 10% versus 20% among all evaluation indexes. Therefore, we can use only 10% labeled data to achieve the experimental objective. CONCLUSIONS: Using the proposed RCBA-UAMT, the CTV of breast cancer CBCT images can be delineated reliably with a small amount of labeled data. These delineated images can be used to observe the changes in CTV and lay the foundation for the follow-up implementation of ART.

Correlation of microscopic tumor extension with tumor microenvironment in esophageal cancer patients.

OBJECTIVE: In the era of image-guided adaptive radiotherapy, definition of the clinical target volume (CTV) is a challenge in various solid tumors, including esophageal cancer (EC). Many tumor microenvironmental factors, e.g., tumor cell proliferation or cancer stem cells, are hypothesized to be involved in microscopic tumor extension (MTE). Therefore, this study assessed the expression of FAK, ILK, CD44, HIF-1α, and Ki67 in EC patients after neoadjuvant radiochemotherapy followed by tumor resection (NRCHT+R) and correlated these markers with the MTE. METHODS: Formalin-fixed paraffin-embedded tumor resection specimens of ten EC patients were analyzed using multiplex immunofluorescence staining. Since gold fiducial markers had been endoscopically implanted at the proximal and distal tumor borders prior to NRCHT+R, correlation of the markers with the MTE was feasible. RESULTS: In tumor resection specimens of EC patients, the overall percentages of FAK+, CD44+, HIF-1α+, and Ki67+ cells were higher in tumor nests than in the tumor stroma, with the outcome for Ki67+ cells reaching statistical significance (p < 0.001). Conversely, expression of ILK+ cells was higher in tumor stroma, albeit not statistically significantly. In three patients, MTE beyond the fiducial markers was found, reaching up to 31 mm. CONCLUSION: Our findings indicate that the overall expression of FAK, HIF-1α, Ki67, and CD44 was higher in tumor nests, whereas that of ILK was higher in tumor stroma. Differences in the TME between patients with residual tumor cells in the original CTV compared to those without were not found. Thus, there is insufficient evidence that the TME influences the required CTV margin on an individual patient basis. TRIAL REGISTRATION NUMBER AND DATE: BO-EK-148042017 and BO-EK-177042022 on 20.06.2022, DRKS00011886, https://drks.de/search/de/trial/DRKS00011886 .

Pelvic target volume inter-fractional motion during radiotherapy for cervical cancer with daily iterative cone beam computed tomography.

BACKGROUND: Tumor regression and organ movements indicate that a large margin is used to ensure target volume coverage during radiotherapy. This study aimed to quantify inter-fractional movements of the uterus and cervix in patients with cervical cancer undergoing radiotherapy and to evaluate the clinical target volume (CTV) coverage. METHODS: This study analyzed 303 iterative cone beam computed tomography (iCBCT) scans from 15 cervical cancer patients undergoing external beam radiotherapy. CTVs of the uterus (CTV-U) and cervix (CTV-C) contours were delineated based on each iCBCT image. CTV-U encompassed the uterus, while CTV-C included the cervix, vagina, and adjacent parametrial regions. Compared with the planning CTV, the movement of CTV-U and CTV-C in the anterior-posterior, superior-inferior, and lateral directions between iCBCT scans was measured. Uniform expansions were applied to the planning CTV to assess target coverage. RESULTS: The motion (mean ± standard deviation) in the CTV-U position was 8.3 ± 4.1 mm in the left, 9.8 ± 4.4 mm in the right, 12.6 ± 4.0 mm in the anterior, 8.8 ± 5.1 mm in the posterior, 5.7 ± 5.4 mm in the superior, and 3.0 ± 3.2 mm in the inferior direction. The mean CTV-C displacement was 7.3 ± 3.2 mm in the left, 8.6 ± 3.8 mm in the right, 9.0 ± 6.1 mm in the anterior, 8.4 ± 3.6 mm in the posterior, 5.0 ± 5.0 mm in the superior, and 3.0 ± 2.5 mm in the inferior direction. Compared with the other tumor (T) stages, CTV-U and CTV-C motion in stage T1 was larger. A uniform CTV planning treatment volume margin of 15 mm failed to encompass the CTV-U and CTV-C in 11.1% and 2.2% of all fractions, respectively. The mean volume change of CTV-U and CTV-C were 150% and 51%, respectively, compared with the planning CTV. CONCLUSIONS: Movements of the uterine corpus are larger than those of the cervix. The likelihood of missing the CTV is significantly increased due to inter-fractional motion when utilizing traditional planning margins. Early T stage may require larger margins. Personal radiotherapy margining is needed to improve treatment accuracy.

Nodal recurrence mapping and clinical target volumes after resection of intrahepatic cholangiocarcinoma or combined hepatocellular-cholangiocarcinoma.

Background: Scarce evidence exists for clinical target volume (CTV) definitions of regional lymph nodes (LNs) in intrahepatic cholangiocarcinoma (iCCA) or combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We investigated the mapping pattern of nodal recurrence after surgery for iCCA and cHCC-CCA and provided evidence for the nodal CTV definition. Methods: We retrospectively reviewed the medical records of patients with iCCA or cHCC-CCA who underwent surgery between 2010 and 2020. Eligibility criteria included patients pathologically diagnosed with iCCA or cHCC-CCA after surgery and a first recurrent event in regional LNs during follow-up. All recurrent LNs were registered onto reference computed tomography images based on the vascular structures to reconstruct the node mapping. Fifty-three patients were eligible. LN regions were classified into four risk groups. Results: Hepatic hilar and portal vein-vena cava were the most common recurrent regions, with recurrence rates of 62.3 % and 39.6 % (high-risk regions), respectively. Recurrence rates in the left gastric, diaphragmatic, common hepatic, superior mesenteric vessels, celiac trunk, and paracardial regions ranged from 15.1 % to 30.2 % (intermediate-risk regions). There were fewer recurrences in the para-aortic (16a1, a2, b1) and splenic artery and hilum regions, with rates <10 % (low-risk regions). No LN recurrence was observed in the para-oesophageal or para-aortic region (16b2) (very low-risk regions). Based on node mapping, the CTV should include high- and intermediate-risk regions for pathologically negative LN patients during postoperative radiotherapy. Low-risk regions should be included for pathologically positive LN patients. Conclusion: We provide evidence for CTV delineation in patients with iCCA and cHCC-CCA based on recurrent LN mapping.

Excluding external iliac node irradiation during neoadjuvant radiotherapy decreases lower intestinal toxicity without compromising efficacy in T4b rectal cancer patients with tumours involving the anterior structures.

PURPOSE: To explore the impact of excluding the external iliac node (EIN) from the clinical target volume (CTV) during preoperative radiotherapy in T4b rectal cancer with anterior structure invasion. METHODS: We retrospectively identified 132 patients with T4b rectal cancer involving the anterior structures who received radiotherapy followed by surgery between May 2010 and June 2019. Twenty-nine patients received EIN irradiation (EIN group), and 103 did not (NEIN group). Failure patterns, survival and toxicities were compared between the two groups. RESULTS: The most common failure was distant metastasis (23.5%). 11 (8.3%) patients developed locoregional recurrence, 10 (9.7%) patients were in the NEIN group, and 1 (3.4%) was in the EIN group (P = 0.34). The EIN region failure was rare (1/132, 0.8%). The locoregional recurrence-free survival (LRFS), distant metastasis-free survival (DMFS), overall survival (OS) and progression-free survival (PFS) rates were 96.3% vs. 90.5%, 82.1% vs.73.7%, 75.9% vs. 78.0% and 72.4% vs. 68.3% (all P > 0.05) for the EIN group and NEIN group, respectively. The incidence of grade 3-4 acute toxicity in the lower intestine was significantly higher in the EIN group than in the NEIN group (13.8% vs. 1.9%, P = 0.02). The Dmax, V35 and V45 of the small bowel was decreased in the NEIN group compared to the EIN group. CONCLUSIONS: Exclusion of the EIN from the CTV in T4b rectal cancer with anterior structure invasion could reduce lower intestinal toxicity without compromising oncological outcomes. These results need further evaluation in future studies.

Proposals for the delineation of neck clinical target volume for definitive Radiation therapy in patients with oral/ oropharyngeal squamous cell cancer based on lymph node distribution.

PURPOSE/OBJECTIVE(S): To establish the distribution pattern of cervical lymph node metastasis (LNM) and propose optimized clinical target volume (CTV) boundaries specific to oral/ oropharyngeal squamous cell cancer (OSCC/OPSCC). MATERIALS/METHODS: 531 patients with pathologically confirmed OSCC/OPSCC were enrolled from January 2013 to June 2022. Patients were stratified into two groups based on the minimal distance from the lesion's edge to the body's midline: ≤1 cm or > 1 cm. The geometric center of cervical metastatic LN was marked on a template CT. LN distribution probability maps were established. The relationships between the LN distribution and consensus guidelines were analyzed to propose modifications for CTV boundaries specific to OSCC/OPSCC. RESULTS: A total of 1962 positive LNs were enrolled. Compared with the > 1 cm group, the ≤ 1 cm group has following feature tendencies: male smokers, younger, median organs, large gross lesion, infiltrative growth pattern, contralateral LNM. The most frequently involved level of LNM was ipsilateral II, but ipsilateral Ib had the highest involvement rate in the > 1 cm OSCC group. In addition, tongue cancer had a higher incidence of LN extranodal extension (ENE), which mainly distributes in ipsilateral level II. The skip metastasis was prone to from level III to Vb (3.5 %) in LN(+)/ENE (-), and level Ib to VIa (3.7 %) in LN(+)/ENE (+). Accordingly, we proposed the following modifications: 1. only including lateral and posterior margin of submandibular gland within 5 mm; 2. retracting posterior boundary of level II to front edge of levator scapula muscle, and descending the upper boundary to transverse process of C2 vertebra only for OSCC; 3. including posterior third of thyroglossal muscle or anterior edge of sternocleidomastoid muscle; 4. sparing level Va in case of only level II involvement; 5. including upper area of the thyroid cartilage plate in case of level Ib LN(+)/ENE (+); 6. sparing level VIIa is considered. CONCLUSION: This is the first description of LN topographic spread patterns for OSCC/OPSCC. Modified CTV for prophylactic irradiation was proposed to spare the organs at risk and minimize adverse effects.

A prior-information-based automatic segmentation method for the clinical target volume in adaptive radiotherapy of cervical cancer.

OBJECTIVE: Adaptive planning to accommodate anatomic changes during treatment often requires repeated segmentation. In this study, prior patient-specific data was integrateda into a registration-guided multi-channel multi-path (Rg-MCMP) segmentation framework to improve the accuracy of repeated clinical target volume (CTV) segmentation. METHODS: This study was based on CT image datasets for a total of 90 cervical cancer patients who received two courses of radiotherapy. A total of 15 patients were selected randomly as the test set. In the Rg-MCMP segmentation framework, the first-course CT images (CT1) were registered to second-course CT images (CT2) to yield aligned CT images (aCT1), and the CTV in the first course (CTV1) was propagated to yield aligned CTV contours (aCTV1). Then, aCT1, aCTV1, and CT2 were combined as the inputs for 3D U-Net consisting of a channel-based multi-path feature extraction network. The performance of the Rg-MCMP segmentation framework was evaluated and compared with the single-channel single-path model (SCSP), the standalone registration methods, and the registration-guided multi-channel single-path (Rg-MCSP) model. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average surface distance (ASD) were used as the metrics. RESULTS: The average DSC of CTV for the deformable image DIR-MCMP model was found to be 0.892, greater than that of the standalone DIR (0.856), SCSP (0.837), and DIR-MCSP (0.877), which were improvements of 4.2%, 6.6%, and 1.7%, respectively. Similarly, the rigid body DIR-MCMP model yielded an average DSC of 0.875, which exceeded standalone RB (0.787), SCSP (0.837), and registration-guided multi-channel single-path (0.848), which were improvements of 11.2%, 4.5%, and 3.2%, respectively. These improvements in DSC were statistically significant (p < 0.05). CONCLUSION: The proposed Rg-MCMP framework achieved excellent accuracy in CTV segmentation as part of the adaptive radiotherapy workflow.

Reliable Delineation of Clinical Target Volumes for Cervical Cancer Radiotherapy on CT/MR Dual-Modality Images.

Accurate delineation of the clinical target volume (CTV) is a crucial prerequisite for safe and effective radiotherapy characterized. This study addresses the integration of magnetic resonance (MR) images to aid in target delineation on computed tomography (CT) images. However, obtaining MR images directly can be challenging. Therefore, we employ AI-based image generation techniques to "intelligentially generate" MR images from CT images to improve CTV delineation based on CT images. To generate high-quality MR images, we propose an attention-guided single-loop image generation model. The model can yield higher-quality images by introducing an attention mechanism in feature extraction and enhancing the loss function. Based on the generated MR images, we propose a CTV segmentation model fusing multi-scale features through image fusion and a hollow space pyramid module to enhance segmentation accuracy. The image generation model used in this study improves the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) from 14.87 and 0.58 to 16.72 and 0.67, respectively, and improves the feature distribution distance and learning-perception image similarity from 180.86 and 0.28 to 110.98 and 0.22, achieving higher quality image generation. The proposed segmentation method demonstrates high accuracy, compared with the FCN method, the intersection over union ratio and the Dice coefficient are improved from 0.8360 and 0.8998 to 0.9043 and 0.9473, respectively. Hausdorff distance and mean surface distance decreased from 5.5573 mm and 2.3269 mm to 4.7204 mm and 0.9397 mm, respectively, achieving clinically acceptable segmentation accuracy. Our method might reduce physicians' manual workload and accelerate the diagnosis and treatment process while decreasing inter-observer variability in identifying anatomical structures.