Solitary Fibrous Tumor With an Acute Subdural Hematoma: A Case Report and Review of the Literature.

Solitary fibrous tumor (SFT) is a rare interstitial tumor that originates from various soft tissues, and SFTs occurring within the cranium are extremely rare. While intracranial SFTs with cerebral hemorrhage or subarachnoid hemorrhage have been reported, there have been no reports of intracranial SFTs causing subdural hematoma. In this case, we report on an intracranial SFT accompanied by a subdural hematoma. A 29-year-old female was emergently transported due to the sudden onset of persistent headache and vomiting that began the night before. CT and MRI imaging revealed a hemorrhagic tumor under the tentorium and an acute subdural hematoma extending along the tentorium. The excised tumor was diagnosed as an SFT through histopathological examination. After undergoing radiation therapy, no recurrence has been observed. This is the first case report of an SFT accompanied by a subdural hematoma, and it is vital to recognize that SFTs can be associated with subdural hematomas for proper diagnosis and treatment planning.

Synthetic Low-Energy Monochromatic Image Generation in Single-Energy Computed Tomography System Using a Transformer-Based Deep Learning Model.

While dual-energy computed tomography (DECT) technology introduces energy-specific information in clinical practice, single-energy CT (SECT) is predominantly used, limiting the number of people who can benefit from DECT. This study proposed a novel method to generate synthetic low-energy virtual monochromatic images at 50 keV (sVMI50keV) from SECT images using a transformer-based deep learning model, SwinUNETR. Data were obtained from 85 patients who underwent head and neck radiotherapy. Among these, the model was built using data from 70 patients for whom only DECT images were available. The remaining 15 patients, for whom both DECT and SECT images were available, were used to predict from the actual SECT images. We used the SwinUNETR model to generate sVMI50keV. The image quality was evaluated, and the results were compared with those of the convolutional neural network-based model, Unet. The mean absolute errors from the true VMI50keV were 36.5 ± 4.9 and 33.0 ± 4.4 Hounsfield units for Unet and SwinUNETR, respectively. SwinUNETR yielded smaller errors in tissue attenuation values compared with those of Unet. The contrast changes in sVMI50keV generated by SwinUNETR from SECT were closer to those of DECT-derived VMI50keV than the contrast changes in Unet-generated sVMI50keV. This study demonstrated the potential of transformer-based models for generating synthetic low-energy VMIs from SECT images, thereby improving the image quality of head and neck cancer imaging. It provides a practical and feasible solution to obtain low-energy VMIs from SECT data that can benefit a large number of facilities and patients without access to DECT technology.

Single-Fraction Palliative High-Dose-Rate Brachytherapy for Symptom Management in a 97-Year-Old Patient With Dementia.

Background: Delivering cancer treatment to elderly patients with dementia is often challenging. We describe performing palliative surface mold brachytherapy (SMBT) in an elderly patient with advanced dementia for pain control using music therapy to assist with agitation. Case Description: The patient was a 97-year-old Japanese woman with advanced dementia. Exudate was observed from her tumor, and she complained of Grade 2 severity pain using Support team assessment schedule (STAS), especially when undergoing would dressings. Given her advanced dementia, she was not considered a candidate for radical surgery or external beam radiotherapy. We instead treated her with high-dose-rate (HDR) SMBT. Due to her advanced dementia associated with agitation, she could not maintain her position. She was able to remain calm while listening to traditional Japanese enka music, which enables our team to complete her radiation without using anesthetics or sedating analgesics. Her localized pain severity decreased ≤21 days and the exudate fluid disappeared ≤63 days after HDR-SMBT. Her tumor was locally controlled until her death from intercurrent disease 1 year after HDR-SMBT. Discussion: Single fraction palliative HDR-SMBT was useful for successful treatment of skin cancer in an elderly patient. Traditional Japanese music helped reduce her agitation to complete HDR-SMBT. For elderly patients with agitation associated with dementia, we should consider using music and music therapy to facilitate radiation therapy.

Artificial intelligence-based image-domain material decomposition in single-energy computed tomography for head and neck cancer.

PURPOSE: While dual-energy computed tomography (DECT) images provide clinically useful information than single-energy CT (SECT), SECT remains the most widely used CT system globally, and only a few institutions can use DECT. This study aimed to establish an artificial intelligence (AI)-based image-domain material decomposition technique using multiple keV-output learning of virtual monochromatic images (VMIs) to create DECT-equivalent images from SECT images. METHODS: This study involved 82 patients with head and neck cancer. Of these, the AI model was built with data from the 67 patients with only DECT scans, while 15 patients with both SECT and DECT scans were used for SECT testing. Our AI model generated VMI50keV and VMI100keV from VMI70keV equivalent to 120-kVp SECT images. We introduced a loss function for material density images (MDIs) in addition to the loss for VMIs. For comparison, we trained the same model with the loss for VMIs only. DECT-equivalent images were generated from SECT images and compared with the true DECT images. RESULTS: The prediction time was 5.4 s per patient. The proposed method with the MDI loss function quantitatively provided more accurate DECT-equivalent images than the model trained with the loss for VMIs only. Using real 120-kVp SECT images, the trained model produced precise DECT images of excellent quality. CONCLUSION: In this study, we developed an AI-based material decomposition approach for head and neck cancer patients by introducing the loss function for MDIs via multiple keV-output learning. Our results suggest the feasibility of AI-based image-domain material decomposition in a conventional SECT system without a DECT scanner.

In Vivo Sonoporation Effect Under the Presence of a Large Amount of Micro-Nano Bubbles in Swine Liver.

OBJECTIVES: Sonoporation as a method of intracellular drug and gene delivery has not yet progressed to being used in vivo. The aim of this study was to prove the feasibility of sonoporation at a level practical for use in vivo by using a large amount of carbon dioxide micro-nano bubbles. METHODS: The carbon dioxide micro-nano bubbles and 100 mg of cisplatin were intra-arterially injected to the swine livers, and ultrasound irradiation was performed from the surface of the liver under laparotomy during the intra-arterial injection. After the intra-arterial injection, ultrasound-irradiated and nonirradiated liver tissues were immediately excised. Tissue platinum concentration was measured using inductively coupled plasma mass spectrometry. Liver tissue platinum concentrations were compared between the irradiated tissue and nonirradiated tissue using the Wilcoxon signed rank test. RESULTS: The mean (SD) liver tissue platinum concentration was 6.260*103 (2.070) ng/g in the irradiated liver tissue and 3.280*103 (0.430) ng/g in the nonirradiated liver tissue, showing significantly higher concentrations in the irradiated tissue (P = 0.004). CONCLUSIONS: In conclusion, increasing the tissue concentration of administered cisplatin in the livers of living swine through the effect of sonoporation was possible in the presence of a large amount of micro-nano bubbles.

A case of pituitary metastasis discovered when diabetes insipidus developed in a patient 20 years after breast cancer treatment.

The patient was a 52-year-old woman. She had a history of left breast cancer at age 32 years, with no recurrences. She was examined for a feeling of oral dryness and nocturia, and central diabetes insipidus was diagnosed. A mass was seen in the posterior pituitary on magnetic resonance imaging, and multiple pulmonary nodules were seen on computed tomography. Breast cancer metastases were diagnosed in both tissues. Since this patient had no cancer other than the breast cancer treated 20 years earlier, it was difficult to reach a diagnosis of pituitary metastasis with pituitary gland imaging alone. In estrogen receptor-positive breast cancer, there may be recurrences after a long period of time. It may be that recommending a full body examination could be useful in the differential diagnosis of metastasis even in patients who have had a long disease-free period, if they had undergone surgery for breast cancer.

Deep learning-based detection of patients with bone metastasis from Japanese radiology reports.

PURPOSE: Deep learning (DL) is a state-of-the-art technique for developing artificial intelligence in various domains and it improves the performance of natural language processing (NLP). Therefore, we aimed to develop a DL-based NLP model that classifies the status of bone metastasis (BM) in radiology reports to detect patients with BM. MATERIALS AND METHODS: The DL-based NLP model was developed by training long short-term memory using 1,749 free-text radiology reports written in Japanese. We adopted five-fold cross-validation and used 200 reports for testing the five models. The accuracy, sensitivity, specificity, precision, and area under the receiver operating characteristics curve (AUROC) were used for the model evaluation. RESULTS: The developed model demonstrated classification performance with mean ± standard deviation of 0.912 ± 0.012, 0.924 ± 0.029, 0.901 ± 0.014, 0.898 ± 0.012, and 0.968 ± 0.004 for accuracy, sensitivity, specificity, precision, and AUROC, respectively. CONCLUSION: The proposed DL-based NLP model may help in the early and efficient detection of patients with BM.

HDR-brachytherapy for accelerated partial breast irradiation: Long-term experience from a Japanese institution.

Purpose: We investigated the long-term oncological outcome of high-dose-rate (HDR) multicatheter interstitial brachytherapy (MIB) for adjuvant accelerated partial breast irradiation (APBI) after breast conserving surgery in Japanese patients. Material and methods: Between June 2002 and October 2011, 86 breast cancer patients were treated at National Hospital Organization Osaka National Hospital (trial number of the local institutional review board, 0329). Median age was 48 years (range, 26-73 years). Eighty patients had invasive and 6 patients non-invasive ductal carcinoma. Tumor stage distribution was pT0 in 2, pTis in 6, pT1 in 55, pT2 in 22, and pT3 in one patient, respectively. Twenty-seven patients had close/positive resection margins. Total physical HDR dose was 36-42 Gy in 6-7 fractions. Results: At a median follow-up of 119 months (range, 13-189 months), the 10-year local control (LC) and overall survival rate was 93% and 88%, respectively. Concerning the 2009 Groupe Européen de Curiethérapie-European Society for Therapeutic Radiology and Oncology risk stratification scheme, the 10-year LC rate was 100%, 100%, and 91% for patients considered as low-risk, intermediate-risk, and high-risk, respectively. According to the 2018 American Brachytherapy Society risk stratification scheme, the 10-year LC rate was 100% and 90% for patients 'acceptable' and 'unacceptable' for APBI, respectively. Wound complications were observed in 7 patients (8%). Risk factors for wound complications were the omission of prophylactic antibiotics during MIB, open cavity implantation, and V100 ≥ 190 cc. No grade ≥ 3 late complications (CTCVE version 4.0) were observed. Conclusions: Adjuvant APBI using MIB is associated with favorable long-term oncological outcomes in Japanese patients for low-risk, intermediate-risk, and acceptable groups of patients.

Artificial intelligence-aided lytic spinal bone metastasis classification on CT scans.

PURPOSE: Spinal bone metastases directly affect quality of life, and patients with lytic-dominant lesions are at high risk for neurological symptoms and fractures. To detect and classify lytic spinal bone metastasis using routine computed tomography (CT) scans, we developed a deep learning (DL)-based computer-aided detection (CAD) system. METHODS: We retrospectively analyzed 2125 diagnostic and radiotherapeutic CT images of 79 patients. Images annotated as tumor (positive) or not (negative) were randomized into training (1782 images) and test (343 images) datasets. YOLOv5m architecture was used to detect vertebra on whole CT scans. InceptionV3 architecture with the transfer-learning technique was used to classify the presence/absence of lytic lesions on CT images showing the presence of vertebra. The DL models were evaluated via fivefold cross-validation. For vertebra detection, bounding box accuracy was estimated using intersection over union (IoU). We evaluated the area under the curve (AUC) of a receiver operating characteristic curve to classify lesions. Moreover, we determined the accuracy, precision, recall, and F1 score. We used the gradient-weighted class activation mapping (Grad-CAM) technique for visual interpretation. RESULTS: The computation time was 0.44 s per image. The average IoU value of the predicted vertebra was 0.923 ± 0.052 (0.684-1.000) for test datasets. In the binary classification task, the accuracy, precision, recall, F1-score, and AUC value for test datasets were 0.872, 0.948, 0.741, 0.832, and 0.941, respectively. Heat maps constructed using the Grad-CAM technique were consistent with the location of lytic lesions. CONCLUSION: Our artificial intelligence-aided CAD system using two DL models could rapidly identify vertebra bone from whole CT images and detect lytic spinal bone metastasis, although further evaluation of diagnostic accuracy is required with a larger sample size.

Effective optimization strategy for large optimization volume object, remaining volume at risk (RVR):α-value selection and usage from generalized equivalent uniform dose (gEUD) curve deviation perspective.

Objective.A large optimization volume for intensity-modulated radiation therapy (IMRT), such as the remaining volume at risk (RVR), is traditionally unsuitable for dose-volume constraint control and requires planner-specific empirical considerations owing to the patient-specific shape. To enable less empirical optimization, the generalized equivalent uniform dose (gEUD) optimization is effective; however, the utilization of parametera-values remains elusive. Our study clarifies thea-value characteristics for optimization and to enable effectivea-value use.Approach.The gEUD can be obtained as a function of itsa-value, which is the weighted generalized mean; its curve has a continuous, differentiable, and sigmoid shape, deforming in its optimization state with retained curve characteristics. Using differential geometry, the gEUD curve changes in optimization is considered a geodesic deviation intervened by the forces between deforming and retaining the curve. The curvature and gradient of the curve are radically related to optimization. The vertex point (a=ak) was set and thea-value roles were classified into the following three parts of the curve with respect to thea-value: (i) high gradient and middle curvature, (ii) middle gradient and high curvature, and (iii) low gradient and low curvature. Then, a strategy for multiplea-values was then identified using RVR optimization.Main results.Eleven head and neck patients who underwent static seven-field IMRT were used to verify thea-value characteristics and curvature effect for optimization. The lowera-value (i) (a= 1-3) optimization was effective for the whole dose-volume range; in contrast, the effect of highera-value (iii) (a= 12-20) optimization addressed strongly the high-dose range of the dose volume. The middlea-value (ii) (arounda=ak) showed intermediate but effective high-to-low dose reduction. Thesea-value characteristics were observed as superimpositions in the optimization. Thus, multiple gEUD-based optimization was significantly superior to the exponential constraints normally applied to the RVR that surrounds the PTV, normal tissue objective (NTO), resulting in up to 25.9% and 8.1% improvement in dose-volume indices D2% and V10Gy, respectively.Significance.This study revealed an appropriatea-value for gEUD optimization, leading to favorable dose-volume optimization for the RVR region using fixed multiplea-value conditions, despite the very large and patient-specific shape of the region.

Patient-specific three-dimensional dose distribution prediction via deep learning for prostate cancer therapy: Improvement with the structure loss.

PURPOSE: Deep learning (DL)-based dose distribution prediction can potentially reduce the cost of inverse planning process. We developed and introduced a structure-focused loss (Lstruct) for 3D dose prediction to improve prediction accuracy. This study investigated the influence of Lstruct on DL-based dose prediction for patients with prostate cancer. The proposed Lstruct, which is similar in concept to dose-volume histogram (DVH)-based optimization in clinical practice, has the potential to provide more interpretable and accurate DL-based optimization. METHODS: This study involved 104 patients who underwent prostate radiotherapy. We used 3D U-Net-based architecture to predict dose distributions from computed tomography and contours of the planning target volume and organs-at-risk. We trained two models using different loss functions: L2 loss and Lstruct. Predicted doses were compared in terms of dose-volume parameters and the Dice similarity coefficient of isodose volume. RESULTS: DVH analysis showed that the Lstruct model had smaller errors from the ground truth than the L2 model. The Lstruct model achieved more consistent dose distributions than the L2 model, with errors close to zero. The isodose Dice score of the Lstruct model was greater than that of the L2 model by >20% of the prescribed dose. CONCLUSIONS: We developed Lstruct using labels of inputted contours for DL-based dose prediction for prostate radiotherapy. Lstruct can be generalized to any DL architecture, thereby enhancing the dose prediction accuracy.

Clinical target volume segmentation based on gross tumor volume using deep learning for head and neck cancer treatment.

Accurate clinical target volume (CTV) delineation is important for head and neck intensity-modulated radiation therapy. However, delineation is time-consuming and susceptible to interobserver variability (IOV). Based on a manual contouring process commonly used in clinical practice, we developed a deep learning (DL)-based method to delineate a low-risk CTV with computed tomography (CT) and gross tumor volume (GTV) input and compared it with a CT-only input. A total of 310 patients with oropharynx cancer were randomly divided into the training set (250) and test set (60). The low-risk CTV and primary GTV contours were used to generate label data for the input and ground truth. A 3D U-Net with a two-channel input of CT and GTV (U-NetGTV) was proposed and its performance was compared with a U-Net with only CT input (U-NetCT). The Dice similarity coefficient (DSC) and average Hausdorff distance (AHD) were evaluated. The time required to predict the CTV was 0.86 s per patient. U-NetGTV showed a significantly higher mean DSC value than U-NetCT (0.80 ± 0.03 and 0.76 ± 0.05) and a significantly lower mean AHD value (3.0 ± 0.5 mm vs 3.5 ± 0.7 mm). Compared to the existing DL method with only CT input, the proposed GTV-based segmentation using DL showed a more precise low-risk CTV segmentation for head and neck cancer. Our findings suggest that the proposed method could reduce the contouring time of a low-risk CTV, allowing the standardization of target delineations for head and neck cancer.

The Dosimetric Analysis of Duodenal and Intestinal Toxicity After a Curative Dose Re-irradiation Using the Intensity-Modulated Radiotherapy for Abdominopelvic Lymph Node Lesions.

INTRODUCTION: This study aimed to examine the influence of dosimetric factors on gastrointestinal toxicity after radical re-irradiation for lymph node recurrence in the abdominopelvic region using a composite plan. METHODS: Between January 2008 and March 2017, 33 patients underwent radical re-irradiation for lymph node recurrence in the abdominopelvic region with a complete overlap with previous radiation therapy (RT) with the median prescription dose of the second RT of 71.7 Gy10. Re-irradiation planning protocol for target volume and organs at risk (OARs) (duodenum, small and large intestines) was decided as follows: more than equal to 97% of the prescription dose was administered to the D95 (percentage of the minimum dose that covered 95% of the target volume) of planning target volume (PTV); minimal dose to the maximally irradiated doses delivered to 1cc [D1 cc] and 5cc [D5 cc] of OARs was set below 70 Gy3 and 50 Gy3, respectively; and D1 cc and D5 cc in the cumulative plans to OARs were 120 Gy3 and 100 Gy3. Kaplan-Meier analyses were performed to evaluate overall survival (OS) and univariate log-rank and multivariate Cox proportional hazards model analyses were performed to explore predictive factors. Using dose summation of the first and re-irradiation plans, we conducted a dosimetric analysis for grade ≥ 3 toxicities of the duodenum and intestine. RESULTS: With a median follow-up of 18 months, the two-year OS rate was 45.5%. The number of RT fields (localized or multiple) was a significant predisposing factor for OS rate with a hazard ratio of 0.23 (95% confidence interval 0.07-0.73). The two-year OS of the patients with a localized RT field was 63.6% and 9.1% for multiple RT fields (p= 0.00007). Four patients experienced grade ≥3 gastrointestinal toxicity related to re-irradiation (4/33=12.1%). We could not find any predisposing dosimetric value in the comparisons with and without toxicity. CONCLUSIONS: The dose constraints presented in this study are relatively low rates of toxicity, which may be useful when planning re-irradiation. Especially, for the patients who could be treated with localized RT field, radical re-irradiation with a high curative dose is a good option. No dosimetric predisposing factor was found for radical re-irradiation of abdominopelvic lesions in the composite plan.

Carcinoma of Unknown Original Identified as Renal Cell Carcinoma by 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Scan: A Report of Two Cases.

Imaging is useful in identifying the primary site of an unknown primary cancer, and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is an excellent imaging modality for identifying the primary lesion. However, a potential limitation is that 18F-FDG is physiologically excreted from the kidneys, thus masking renal lesions. In this report, we describe two cases of cancer of unknown origin that were detected as originating from renal cancer on 18F-FDG PET/CT. Both cases showed abnormal nodular accumulation of 18F-FDG in the kidney, which can be distinguished from the physiological excretion of 18F-FDG in the urinary tract. It is clinically crucial to be able to confirm the possibility of renal cancer, and careful observation of the urinary tract with 18F-FDG PET/CT can be useful.

Venous Rupture Following Transcatheter Arterial Embolization for Inferior Mesenteric Type II Arteriovenous Malformation.

We treated a 64-year-old man who had an inferior mesenteric arteriovenous malformation with multiple shunts. As multiple varicosities in the draining vein became enlarged, two dilated shunts on the superior rectal and sigmoid colon arteries were coil embolized. Two days after embolization, a varicosity near the shunt (65 mm diameter) ruptured, causing intra-abdominal hemorrhage and surgical hemostasis. There were thrombi in the ruptured varicosity and its draining vein. The likely cause was a pressure increase in the incompletely thrombosed varicosity due to shunt blood flow from the remaining shunts after embolization.

Radiation Exposure and Protection in Computed Tomography Fluoroscopy.

Computed tomography (CT) fluoroscopy-guided procedures, such as those used for percutaneous biopsy, drainage, and radiofrequency ablation, are highly safe and quite often very successful due to the precision offered by the real-time, high-resolution tomographic images. Even so, international guidelines raised concerns regarding operator exposure to high doses of radiation during these procedures. In light of these concerns, operators conducting CT fluoroscopy-guided procedures not only need to be cognizant of the exposure risk but also exhibit sufficient knowledge of radiation protection. This paper reviews the current literature on experimental and clinical studies of radiation exposure doses to operators during CT fluoroscopy-guided procedures. In addition to the literature review, this paper also introduces different approaches that can be implemented to ensure appropriate radiation protection.

Percutaneous Transsplenic Embolization of Gastric Varices in Left-sided Portal Hypertension.

An 81-year-old man with previously diagnosed cancer of the pancreatic body presented with melena and anemia. Upper gastrointestinal endoscopy showed gastric varices with bleeding in the entire stomach. Contrast-enhanced computed tomography identified a splenic vein occlusion resulting from invasion by the pancreatic body cancer and dilated collateral pathways from the splenic hilum to the gastric fundus. The patient was diagnosed with gastric varices associated with left-sided portal hypertension caused by obstruction of the splenic vein and underwent percutaneous transsplenic embolization with n-butyl-2-cyanoacrylate mixed with lipiodol. Splenic subcapsular hematoma occurred and was treated conservatively. The patient died of advanced cancer 5 months after the procedure, without experiencing rebleeding. Percutaneous transsplenic embolization was effective in treating gastric variceal bleeding caused by left-sided portal hypertension.

Practical Radiation Protection for Interventional Radiologist.

As per the International Commission on Radiological Protection 2010 recommendation, it was stated that "interventional radiologists performing difficult procedures with high workloads may be exposed to high doses" and that education and training of medical staffs in radiation exposure is "an urgent priority." There are many reports on the textbook aspects of radiation protection, but reports on the practical aspects of radiation protection have remained to be scarce. Various methods of reducing radiation exposure are described as "useful" or "can be reduced," but the priority of these methods and the "extent" to which they contribute to reducing radiation exposure are not clear. Thus, in this article, we will look into the protection of interventional radiologist from radiation exposure in a practical way, giving priority to clarity rather than academic accuracy.

Efficacy of Combination Therapy with Lenvatinib and Radioactive Iodine in Thyroid Cancer Preclinical Model.

Patients with differentiated thyroid cancer (DTC) usually have good prognosis, while those with advanced disease have poor clinical outcomes. This study aimed to investigate the antitumor effects of combination therapy with lenvatinib and 131I (CTLI) using three different types of DTC cell lines with different profiling of sodium iodide symporter (NIS) status. The radioiodine accumulation study revealed a significantly increased radioiodine uptake in K1-NIS cells after lenvatinib treatment, while there was almost no uptake in K1 and FTC-133 cells. However, lenvatinib administration before radioiodine treatment decreased radioiodine uptake of K1-NIS xenograft tumor in the in vivo imaging study. CTLI synergistically inhibited colony formation and DTC cell migration, especially in K1-NIS cells. Finally, 131I treatment followed by lenvatinib administration significantly inhibited tumor growth of the NIS-expressing thyroid cancer xenograft model. These results provide important clinical implications for the combined therapy that lenvatinib should be administered after 131I treatment to maximize the treatment efficacy. Our synergistic treatment effects by CTLI suggested its effectiveness for RAI-avid thyroid cancer, which retains NIS function. This potential combination therapy suggests a powerful and tolerable new therapeutic strategy for advanced thyroid cancer.

A Case of Percutaneous Transesophageal Jejunostomy after Subtotal Esophagectomy.

An 80-year-old woman who underwent subtotal esophagectomy with gastric tube reconstruction for esophageal cancer developed carcinoma of the left upper gingiva. The local recurrence of the gingival carcinoma resulted in trismus and prevented oral intake. Then she underwent a percutaneous transesophageal jejunostomy tube placement in the preserved cervical esophagus. Enteral feeding continued for three months with no complications until oral intake was possible. A percutaneous transesophageal jejunostomy is possible using the postoperatively preserved cervical esophagus.