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Radiation therapy used in the treatment of cancer causes skin damage, and no method of care has been established thus far. Recently, it has become clear that sericin derived from silkworm cocoons has moisturizing and antioxidant functions. In addition, green cocoon-derived sericin, which is rich in flavonoids, may have enhanced functions. However, whether this green cocoon-derived sericin can reduce radiotherapy-induced skin damage is unclear. In the present study, we aimed at establishing care methods to reduce skin cell damage caused by X-irradiation using green cocoon-derived sericin. We investigated its effect on human keratinocytes using lactate dehydrogenase activity to indicate damage reduction. Our results showed that green cocoon-derived sericin reduced cell damage caused by X-irradiation. However, this effect was not observed when cells were treated before X-irradiation or with a sericin derived from white cocoons. In addition, green cocoon-derived sericin decreased the levels of reactive oxygen species and lipid peroxidation. Our results suggest that green cocoon sericin mitigates the damaging effect of X-irradiation on cells, hence presenting potential usefulness in reducing skin damage from radiation therapy and opening new avenues in the care of cancer patients., (© 2024. The Author(s).)

Purpose: We sought to develop machine learning models to predict the results of patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), which were represented by several dose-evaluation metrics-including the gamma passing rates (GPRs)-and criteria based on the radiomic features of 3D dose distribution in a phantom., Methods: A total of 4,250 radiomic features of 3D dose distribution in a cylindrical dummy phantom for 140 arcs from 106 clinical VMAT plans were extracted. We obtained the following dose-evaluation metrics: GPRs with global and local normalization, the dose difference (DD) in 1% and 2% passing rates (DD1% and DD2%) for 10% and 50% dose threshold, and the distance-to-agreement in 1-mm and 2-mm passing rates (DTA1 mm and DTA2 mm) for 0.5%/mm and 1.0%.mm dose gradient threshold determined by measurement using a diode array in patient-specific QA. The machine learning regression models for predicting the values of the dose-evaluation metrics using the radiomic features were developed based on the elastic net (EN) and extra trees (ET) models. The feature selection and tuning of hyperparameters were performed with nested cross-validation in which four-fold cross-validation is used within the inner loop, and the performance of each model was evaluated in terms of the root mean square error (RMSE), the mean absolute error (MAE), and Spearman's rank correlation coefficient., Results: The RMSE and MAE for the developed machine learning models ranged from <1% to nearly <10% depending on the dose-evaluation metric, the criteria, and dose and dose gradient thresholds used for both machine learning models. It was advantageous to focus on high dose region for predicating global GPR, DDs, and DTAs. For certain metrics and criteria, it was possible to create models applicable for patients' heterogeneity by training only with dose distributions in phantom., Conclusions: The developed machine learning models showed high performance for predicting dose-evaluation metrics especially for high dose region depending on the metric and criteria. Our results demonstrate that the radiomic features of dose distribution can be considered good indicators of the plan complexity and useful in predicting measured dose evaluation metrics., (© 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: The purpose of this study was to create and evaluate deep learning-based models to detect and classify errors of multi-leaf collimator (MLC) modeling parameters in volumetric modulated radiation therapy (VMAT), namely the transmission factor (TF) and the dosimetric leaf gap (DLG)., Methods: A total of 33 clinical VMAT plans for prostate and head-and-neck cancer were used, assuming a cylindrical and homogeneous phantom, and error plans were created by altering the original value of the TF and the DLG by ± 10, 20, and 30% in the treatment planning system (TPS). The Gaussian filters of σ = 0.5 $\sigma = 0.5$ and 1.0 were applied to the planar dose maps of the error-free plan to mimic the measurement dose map, and thus dose difference maps between the error-free and error plans were obtained. We evaluated 3 deep learning-based models, created to perform the following detections/classifications: (1) error-free versus TF error, (2) error-free versus DLG error, and (3) TF versus DLG error. Models to classify the sign of the errors were also created and evaluated. A gamma analysis was performed for comparison., Results: The detection and classification of TF and DLG error were feasible for σ = 0.5 $\sigma = 0.5$ ; however, a considerable reduction of accuracy was observed for σ = 1.0 $\sigma = 1.0$ depending on the magnitude of error and treatment site. The sign of errors was detectable by the specifically trained models for σ = 0.5 $\sigma = 0.5$ and 1.0. The gamma analysis could not detect errors., Conclusions: We demonstrated that the deep learning-based models could feasibly detect and classify TF and DLG errors in VMAT dose distributions, depending on the magnitude of the error, treatment site, and the degree of mimicked measurement doses., (© 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)

Purpose: The liver is the largest organ in the abdomen and is often irradiated in radiotherapy for non-hepatic malignancies. As most of the studies on changes in liver volume are on hepatocellular carcinoma based on liver dysfunction, there are few studies on healthy liver. In this study, we investigated the relationship between absorbed dose and changes in liver volume after chemoradiotherapy for esophageal cancer in patients without apparent pre-treatment liver dysfunction., Materials and Methods: Liver volume was compared between pre-treatment, acute (< 4 months) and late post-treatment (≥ 4 and < 13 months) phases in 12 patients using abdominal plain CT images. Volume changes were evaluated separately for the right and left lobes. We investigated the relationship between the volume change and V xGy (percentage of volume received x Gy or more dose). In addition, volume change for each absorbed dose was investigated using deformable image registration., Results: The volume of the left lobe showed a significant decrease between pre-treatment and acute post-treatment phases (p < 0.001), while the volume of right lobe and between acute and late post-treatment phase of left lobe did not. The mean value of the volume reduction rate of the left lobe was 51.1% and equivalent to the mean value of V 30Gy . As a result of the volume change for each absorbed dose, the volume reduction rate increased as the absorbed dose increased, and a significant volume loss was observed at doses above 11 Gy., Conclusion: Volume of the liver significantly decreased only in the acute phase after chemoradiotherapy for esophageal cancer. The tolerable dose for a healthy liver is generally considered to be 30 Gy, but attention should be paid to lower doses to avoid radiation-induced liver injury., (© 2022. The Author(s) under exclusive licence to Japan Radiological Society.)

Cadmium is adsorbed on calcium carbonate via chemisorption. All calcium carbonate polymorphs generate otavite (cadmium carbonate), indicating that the crystallographic differences in calcium carbonate should affect the chemisorption equilibrium and kinetics. This study investigates the influences of the polymorph and specific surface area on cadmium adsorption. Here, we synthesise two polymorphs of porous calcium carbonate: calcite and vaterite with a wide range of specific surface areas. Then the equilibrium of cadmium adsorption is evaluated using adsorption isotherm models. Based on the Langmuir model with linear regression analysis, the maximum adsorptions of porous calcite and vaterite particles are 287.8 mg/g and 883.5 mg/g, respectively. The kinetics of cadmium chemisorption show clear differences between polymorphs. The calculated rate constant of the porous calcite particles using a pseudo-second-order reaction and Elovich models are two orders larger than that of porous vaterite particles. Although the adsorbed amount is superior for porous vaterite particles, porous calcite particles exhibit a faster reaction and relatively high adsorbed capacity for cadmium ions., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Previous studies on cadmium adsorption of calcium carbonate have found that polymorph, and, crystallinity are influential factors for adsorbing cadmium ions. The predominant factor for cadmium adsorption has yet to be elucidated because these factors are linked. To overcome this, here each factor is investigated separately. First, atmospheric grinding prepared surf clam (aragonite phase) and scallop (calcite phase) shells with similar crystallite sizes and specific surface areas. Using adsorption isotherm models, kinetics, X-ray diffraction analysis, and TEM observations, both calcite and aragonite react with cadmium to form cadmium carbonate. The chemisorption follows the adsorption mechanism reported in the literature. Based on the Langmuir isotherm model fitting, the maximum adsorbed amount for the ground surf clam shells is 633.3 mg/g, while that for scallop shells is 195.8 mg/g. Then fine surf clam shell particles with a similar specific surface area, and with a relatively wide range of the aragonite ratio, and crystallite size are prepared via a combination of grinding and a subsequent calcination process. Our experiments where one explanatory variable is changed at a time demonstrate that the polymorph ratio and crystallite size of the ground shells play key roles in the chemisorption., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose: We calculated the dosimetric indices and estimated the tumor control probability (TCP) considering six degree-of-freedom (6DoF) patient setup errors in stereotactic radiosurgery (SRS) using a single-isocenter technique., Methods: We used simulated spherical gross tumor volumes (GTVs) with diameters of 1.0 cm (GTV 1), 2.0 cm (GTV 2), and 3.0 cm (GTV 3), and the distance (d) between the target center and isocenter was set to 0, 5, and 10 cm. We created the dose distribution by convolving the blur component to uniform dose distribution. The prescription dose was 20 Gy and the dose distribution was adjusted so that D95 (%) of each GTV was covered by 100% of the prescribed dose. The GTV was simultaneously rotated within 0°-1.0° (δR) around the x-, y-, and z-axes and then translated within 0-1.0 mm (δT) in the x-, y-, and z-axis directions. D95, conformity index (CI), and conformation number (CN) were evaluated by varying the distance from the isocenter. The TCP was estimated by translating the calculated dose distribution into a biological response. In addition, we derived the x-y-z coordinates with the smallest TCP reduction rate that minimize the sum of squares of the residuals as the optimal isocenter coordinates using the relationship between 6DoF setup error, distance from isocenter, and GTV size., Results: D95, CI, and CN were decreased with increasing isocenter distance, decreasing GTV size, and increasing setup error. TCP of GTVs without 6DoF setup error was estimated to be 77.0%. TCP were 25.8% (GTV 1), 35.0% (GTV 2), and 53.0% (GTV 3) with (d, δT , δR) = (10 cm, 1.0 mm, 1.0°). The TCP was 52.3% (GTV 1), 54.9% (GTV 2), and 66.1% (GTV 3) with (d, δT , δR) = (10 cm, 1.0 mm, 1.0°) at the optimal isocenter position., Conclusion: The TCP in SRS for multiple brain metastases with a single-isocenter technique may decrease with increasing isocenter distance and decreasing GTV size when the 6DoF setup errors are exceeded (1.0 mm, 1.0°). Additionally, it might be possible to better maintain TCP for GTVs with 6DoF setup errors by using the optimal isocenter position., (© 2021 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

We aimed to compare the outcomes of high-dose-rate brachytherapy (HDR-BT) boost and external beam radiation therapy (EBRT) alone for high-risk prostate cancer. This was a single-center, retrospective and observational study. Consecutive patients who underwent initial radical treatment by HDR-BT boost or EBRT alone from June 2009 to May 2016 at the Niigata University Medical and Dental Hospital, Japan were included. A total of 96 patients underwent HDR-BT boost, and 61 underwent EBRT alone. The prescription dose of HDR-BT boost was set to 18 Gy twice a day with EBRT 39 Gy/13 fractions. The dose for EBRT alone was mostly 70 Gy/28 fractions. The high-risk group received >6 months of prior androgen deprivation therapy. Overall survival, biochemical-free survival, local control and distant metastasis-free survival rates at 5 years were analyzed. The incidence of urological and gastrointestinal late adverse events of Grade 2 and above was also summarized. In the National Comprehensive Cancer Network (NCCN) high-risk calssification, HDR-BT boost had a significantly higher biochemical-free survival rate at 5 years (98.9% versus 90.7%, P = 0.04). Urethral strictures were more common in the HDR-BT boost group. We will continuously observe the progress of the study patients and determine the longer term results., (© The Author(s) 2021. Published by Oxford University Press on behalf of The Japanese Radiation Research Society and Japanese Society for Radiation Oncology.)

Purpose: We sought to develop machine learning models to detect multileaf collimator (MLC) modeling errors with the use of radiomic features of fluence maps measured in patient-specific quality assurance (QA) for intensity-modulated radiation therapy (IMRT) with an electric portal imaging device (EPID)., Methods: Fluence maps measured with EPID for 38 beams from 19 clinical IMRT plans were assessed. Plans with various degrees of error in MLC modeling parameters [i.e., MLC transmission factor (TF) and dosimetric leaf gap (DLG)] and plans with an MLC positional error for comparison were created. For a total of 152 error plans for each type of error, we calculated fluence difference maps for each beam by subtracting the calculated maps from the measured maps. A total of 837 radiomic features were extracted from each fluence difference map, and we determined the number of features used for the training dataset in the machine learning models by using random forest regression. Machine learning models using the five typical algorithms [decision tree, k-nearest neighbor (kNN), support vector machine (SVM), logistic regression, and random forest] for binary classification between the error-free plan and the plan with the corresponding error for each type of error were developed. We used part of the total dataset to perform fourfold cross-validation to tune the models, and we used the remaining test dataset to evaluate the performance of the developed models. A gamma analysis was also performed between the measured and calculated fluence maps with the criteria of 3%/2 and 2%/2 mm for all of the types of error., Results: The radiomic features and its optimal number were similar for the models for the TF and the DLG error detection, which was different from the MLC positional error. The highest sensitivity was obtained as 0.913 for the TF error with SVM and logistic regression, 0.978 for the DLG error with kNN and SVM, and 1.000 for the MLC positional error with kNN, SVM, and random forest. The highest specificity was obtained as 1.000 for the TF error with a decision tree, SVM, and logistic regression, 1.000 for the DLG error with a decision tree, logistic regression, and random forest, and 0.909 for the MLC positional error with a decision tree and logistic regression. The gamma analysis showed the poorest performance in which sensitivities were 0.737 for the TF error and the DLG error and 0.882 for the MLC positional error for 3%/2 mm. The addition of another type of error to fluence maps significantly reduced the sensitivity for the TF and the DLG error, whereas no effect was observed for the MLC positional error detection., Conclusions: Compared to the conventional gamma analysis, the radiomics-based machine learning models showed higher sensitivity and specificity in detecting a single type of the MLC modeling error and the MLC positional error. Although the developed models need further improvement for detecting multiple types of error, radiomics-based IMRT QA was shown to be a promising approach for detecting the MLC modeling error., (© 2020 American Association of Physicists in Medicine.)

Through geometrical simulation, we evaluated the effect of rotational error in patient setup on geometrical coverage and calculated the maximum distance between the isocenter and target, where the clinical PTV margin secures geometrical coverage with a single-isocenter technique. We used simulated spherical GTVs with diameters of 1.0 (GTV 1), 1.5 (GTV 2), 2.0 (GTV 3), and 3.0 cm (GTV 4). The location of the target center was set such that the distance between the target and isocenter ranged from 0 to 15 cm. We created geometrical coverage vectors so that each target was entirely covered by 100% of the prescribed dose. The vectors of the target positions were simultaneously rotated within a range of 0°-2.0° around the x-, y-, and z-axes. For each rotational error, the reduction in geometrical coverage of the targets was calculated and compared with that obtained for a rotational error of 0°. The tolerance value of the geometrical coverage reduction was defined as 5% of the GTV. The maximum distance that satisfied the 5% tolerance value for different values of rotational error at a clinical PTV margin of 0.1 cm was calculated. When the rotational errors were 0.5° for a 0.1 cm PTV margin, the maximum distances were as follows: GTV 1: 7.6 cm; GTV 2: 10.9 cm; GTV 3: 14.3 cm; and GTV 4: 21.4 cm. It might be advisable to exclude targets that are > 7.6 cm away from the isocenter with a single-isocenter technique to satisfy the tolerance value for all GTVs.

In conventional stereotactic radiosurgery (SRS), treatment of multiple brain metastases using multiple isocenters is time-consuming resulting in long dose delivery times for patients. A single-isocenter technique has been developed which enables the simultaneous irradiation of multiple targets at one isocenter. This technique requires accurate positioning of the patient to ensure optimal dose coverage. We evaluated the effect of six degrees of freedom (6DoF) setup errors in patient setups on SRS dose distributions for multiple brain metastases using a single-isocenter technique. We used simulated spherical gross tumor volumes (GTVs) with diameters ranging from 1.0 to 3.0 cm. The distance from the isocenter to the target's center was varied from 0 to 15 cm. We created dose distributions so that each target was entirely covered by 100% of the prescribed dose. The target's position vectors were rotated from 0°-2.0° and translated from 0-1.0 mm with respect to the three axes in space. The reduction in dose coverage for the targets for each setup error was calculated and compared with zero setup error. The calculated margins for the GTV necessary to satisfy the tolerance values for loss of GTV coverage of 3% to 10% were defined as coverage-based margins. In addition, the maximum isocenter to target distance for different 6DoF setup errors was calculated to satisfy the tolerance values. The dose coverage reduction and coverage-based margins increased as the target diameter decreased, and the distance and 6DoF setup error increased. An increase in setup error when a single-isocenter technique is used may increase the risk of missing the tumor; this risk increases with increasing distance from the isocenter and decreasing tumor size., (© 2020 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Background and Purpose: Concurrent chemoradiotherapy (CCRT) for head and neck cancer (HNC) is a risk factor for oral candidiasis (OC). As Candida spp. are highly virulent, we conducted a retrospective study to determine whether OC increases the severity of dysphagia related to mucositis in HNC patients., Patients and Methods: We retrospectively analyzed the cases of consecutive patients with carcinomas of the oral cavity, pharynx, and larynx who underwent CCRT containing cisplatin (CDDP) at our hospital. The diagnosis of OC was based on gross mucosal appearance. We performed a multivariate analysis to determine whether OC was associated with the development of grade 3 dysphagia in the Radiation Therapy Oncology Group (RTOG) Acute Toxicity Criteria. The maximum of the daily opioid doses was compared between the patients with and without OC., Results: We identified 138 HNC patients. OC was observed in 51 patients (37%). By the time of their OC diagnosis, 19 (37%) had already developed grade 3 dysphagia. Among the 30 patients receiving antifungal therapy, 12 (40%) showed clinical deterioration. In the multivariate analysis, OC was independently associated with grade 3 dysphagia (OR 2.75; 95%CI 1.22-6.23; p = 0.015). The patients with OC required significantly higher morphine-equivalent doses of opioids (45 vs. 30 mg/day; p = 0.029)., Conclusion: Candida infection causes refractory dysphagia. It is worth investigating whether antifungal prophylaxis reduces severe dysphagia related to candidiasis., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2019 The Author(s).)

There are no quantitative selection criteria for identifying high-grade glioma (HGG) patients who are suited for volumetric-modulated arc therapy (VMAT). This study aimed to develop selection criteria that can be used for the selection of the optimal treatment modality in HGG. We analyzed 20 patients with HGG treated by 3D conformal radiotherapy (3DCRT). First, VMAT plans were created for each patient retrospectively. For each plan, the normal tissue complication probability (NTCP) for normal brain was calculated. We then divided the patients based on the NTCPs of the 3DCRT plans for normal brain, using the threshold of 5%. We compared the NTCPs of the two plans and the gross tumor volumes (GTVs) of the two groups. For the GTVs, we used receiver operating characteristic curves to identify the cut-off value for predicting NTCP < 5%. We determined the respective correlations between the GTV and the GTV's largest cross-sectional diameter and largest cross-sectional area. In the NTCP ≥ 5% group, the NTCPs for the VMAT plans were significantly lower than those for the 3DCRT plans (P = 0.0011). The NTCP ≥ 5% group's GTV was significantly larger than that of the NTCP < 5% group (P = 0.0016), and the cut-off value of the GTV was 130.5 cm3. The GTV was strongly correlated with the GTV's largest cross-sectional diameter (R2 = 0.82) and largest cross-sectional area (R2 = 0.94), which produced the cut-off values of 7.5 cm and 41 cm2, respectively. It was concluded that VMAT is more appropriate than 3DCRT in cases in which the GTV is ≥130.5 cm3., (© The Author(s) 2019. Published by Oxford University Press on behalf of The Japan Radiation Research Society and Japanese Society for Radiation Oncology.)

In X-ray therapy, equivalent square field (side of equivalent square field) is important because it influences the accuracy of independent verification of monitor unit (MU) by calculation. To calculate the side of equivalent square field for rectangular fields, we often use a table of domestic standard measurement method (Day's method), or A/P method calculated by area-perimeter ratio. The sides of equivalent square fields of these methods are assumed to be unchanged by depth and energy, but there are reports that it is not valid. Therefore, the depth dependency of side of equivalent square fields of Day's method, A/P method, and area ratio correction (ARC) method was compared by measuring phantom scatter factors (S p ). From the analysis of S p measured at different depths, the estimated value of S p on the equivalent square side of the Day's method and A/P method had a depth dependency that the difference from the measured value was large when the measurement depth was deep. The estimated value of S p on the equivalent square side of the ARC method had a small difference from the measured value even when the measurement depth was deep, and the depth dependency was small compared with the Day's method and the A/P method. Side of equivalent square field of ARC method had a smaller difference of depth dependency than in the case of Day's method and A/P method. Therefore, in the independent verification of MU for rectangular field, using the equivalent square side of the ARC method is better.

In monitor unit (MU) independent verification by calculation for irregular field (MLC field) using multileaf collimator in X-ray therapy, it has become common to use collimator scatter factor (S c ) and phantom scatter factor (S p ) instead of total scatter factor (S c, p ). It is usually expressed as S c, p (A)=S c (A)×S p (A), and the field size A is considered but the depth d is not. S c is data of in-air output, and measure with a mini-phantom at constant depth to remove electron contamination. On the other hand, S p is obtained from measurement data of S c, p and S c , and can be expressed as S c, p (d, A)=S c (constant depth, A)×S p (d, A) at an arbitrary depth d, thus S p depends on the depth of S c, p . Therefore, S p needs to consider depth. In addition, a linear accelerator equipped with the tertiary MLC has two field sizes, that are collimator field by upper and lower collimators and MLC field by tertiary MLC below them. In MU independent verification by calculation, it is often used that the estimated value of S p obtained by converting MLC field to equivalent square field and referring to data of S p in square field. To convert the MLC field to equivalent square field, a conversion formula from sector radius r to equivalent square field L by Clarkson's sector integration (Clarkson method) is used. In this study, using 24 types of MLC fields to evaluate estimation accuracy due to the difference of conversion formula in Clarkson method, we estimated value of S p using r=0.5611L of B-Clarkson method and using r=0.5580L of A-Clarkson method. And the difference with the measured value of S p obtained by measuring S c, p and S c in the same MLC fields was compared. While, to evaluate estimation accuracy due to the different depths using these Clarkson methods, the difference between estimated value and measured value of S p similarly obtained at depth of 5, 10 and 15 cm was compared. As results, estimated value of S p using A-Clarkson method than using B-Clarkson method was close to measured value, and it was the same trend at depth of 5, 10 and 15 cm. Therefore, it was suggested that estimation accuracy of S p by A-Clarkson method is higher than B-Clarkson method when verifying beams with different depths in MU independent verification by calculation for MLC field.

Rationale: Gastric adenocarcinoma of fundic gland type (GA-FG) is a new histological type of gastric cancer manifesting with differentiation into a fundic gland. Furthermore, gastric adenocarcinoma of fundic gland mucosa type (GA-FGM) is a tumor that shows differentiation into not only a fundic gland but also foveolar epithelium and a mucous gland. These tumors tend to invade the submucosal layer. However, no cases of these tumors being localized only in the submucosa have been reported. Here, we present a case of GA-FGM localized in the submucosa and describe the cytological features of this tumor. To our knowledge, this is the first reported case of GA-FGM localized in the submucosa., Patient Concerns: A man in his early 70s was referred to our institution because of the detection of a gastric submucosal tumor during a health checkup., Diagnoses: Gastric adenocarcinoma of fundic gland mucosa type., Interventions: Endoscopic ultrasound-guided fine-needle aspiration (FNA), endoscopic submucosal dissection (ESD), and total gastrectomy with lymph node dissection were performed., Outcomes: The FNA specimen showed epithelial cells with low-grade atypia. In the ESD specimen, adenocarcinoma showing a gastric fundic gland mucosa-like morphology was observed. Immunohistochemical analysis showed positive staining for pepsinogen I, H+/K+-adenosine triphosphatase, MUC6, and MUC5AC and negative staining for MUC2 and CD10, indicating tumor differentiation into fundic gland mucosa. Therefore, the tumor was diagnosed as GA-FGM, with localization in the submucosal layer. Total gastrectomy and lymph node dissection were performed because of the positive margins of the ESD specimen. Neither residual tumor nor lymph node metastasis was detected; however, many foci of heterotopic gastric glands (HGGs) were observed in the gastric wall, suggesting that GA-FGM arose from an HGG. After treatment, no recurrence was observed during a 1-year follow-up period., Lessons: Various tumors may arise from HGGs. Furthermore, when an FNA specimen shows gastric fundic gland mucosa-like epithelial cells with weak atypia, the possibility of GA-FG and GA-FGM should be considered.