Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging

AbstractThe diagnosis of gastrointestinal stromal tumor (GIST) using conventional endoscopy is difficult because submucosal tumor (SMT) lesions like GIST are covered by a mucosal layer. Near-infrared hyperspectral imaging (NIR-HSI) can obtain optical information from deep inside tissues. However, far less progress has been made in the development of techniques for distinguishing deep lesions like GIST. This study aimed to investigate whether NIR-HSI is suitable for distinguishing deep SMT lesions. In this study, 12 gastric GIST lesions were surgically resected and imaged with an NIR hyperspectral camera from the aspect of the mucosal surface. Thus, the images were obtained ex-vivo. The site of the GIST was defined by a pathologist using the NIR image to prepare training data for normal and GIST regions. A machine learning algorithm, support vector machine, was then used to predict normal and GIST regions. Results were displayed using color-coded regions. Although 7 specimens had a mucosal layer (thickness 0.4–2.5 mm) covering the GIST lesion, NIR-HSI analysis by machine learning showed normal and GIST regions as color-coded areas. The specificity, sensitivity, and accuracy of the results were 73.0%, 91.3%, and 86.1%, respectively. The study suggests that NIR-HSI analysis may potentially help distinguish deep lesions.

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Author Correction: Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging

Scientific Reports ◽

10.1038/s41598-021-98867-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Daiki Sato ◽

Toshihiro Takamatsu ◽

Masakazu Umezawa ◽

Yuichi Kitagawa ◽

Kosuke Maeda ◽

...

Keyword(s):

Gastrointestinal Stromal Tumor ◽

Hyperspectral Imaging ◽

Near Infrared ◽

Stromal Tumor

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Application of Near-Infrared Hyperspectral Imaging with Machine Learning Methods to Identify Geographical Origins of Dry Narrow-Leaved Oleaster (Elaeagnus angustifolia) Fruits

Foods ◽

10.3390/foods8120620 ◽

2019 ◽

Vol 8 (12) ◽

pp. 620 ◽

Cited By ~ 3

Author(s):

Pan Gao ◽

Wei Xu ◽

Tianying Yan ◽

Chu Zhang ◽

Xin Lv ◽

...

Keyword(s):

Machine Learning ◽

Hyperspectral Imaging ◽

Near Infrared ◽

Support Vector ◽

Elaeagnus Angustifolia ◽

Learning Methods ◽

Machine Learning Methods ◽

Discriminant Models ◽

Second Derivative Spectra ◽

Origin Identification

Narrow-leaved oleaster (Elaeagnus angustifolia) fruit is a kind of natural product used as food and traditional medicine. Narrow-leaved oleaster fruits from different geographical origins vary in chemical and physical properties and differ in their nutritional and commercial values. In this study, near-infrared hyperspectral imaging covering the spectral range of 874–1734 nm was used to identify the geographical origins of dry narrow-leaved oleaster fruits with machine learning methods. Average spectra of each single narrow-leaved oleaster fruit were extracted. Second derivative spectra were used to identify effective wavelengths. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to build discriminant models for geographical origin identification using full spectra and effective wavelengths. In addition, deep convolutional neural network (CNN) models were built using full spectra and effective wavelengths. Good classification performances were obtained by these three models using full spectra and effective wavelengths, with classification accuracy of the calibration, validation, and prediction set all over 90%. Models using effective wavelengths obtained close results to models using full spectra. The performances of the PLS-DA, SVM, and CNN models were close. The overall results illustrated that near-infrared hyperspectral imaging coupled with machine learning could be used to trace geographical origins of dry narrow-leaved oleaster fruits.

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A Gastrointestinal Stromal Tumor of the Stomach Demonstrating a Stepwise Progression from Low- to High-Grade Malignancy

Case Reports in Gastrointestinal Medicine ◽

10.1155/2012/606832 ◽

2012 ◽

Vol 2012 ◽

pp. 1-3 ◽

Cited By ~ 2

Author(s):

Takahiko Nakajima ◽

Tomonori Ushijima ◽

Atsushi Kihara ◽

Kenichiro Murata ◽

Toshiro Sugiyama ◽

...

Keyword(s):

Gastrointestinal Stromal Tumor ◽

Clinical Course ◽

Submucosal Tumor ◽

Histological Finding ◽

Stromal Tumor ◽

Gastric Gist ◽

High Grade ◽

Grade Malignancy ◽

Multiple Liver Metastases ◽

High Grade Malignancy

We report a case of a gastrointestinal stromal tumor (GIST) of the stomach that demonstrated a stepwise progression from low- to high-grade malignancy. The patient had been followed for a small gastric submucosal tumor that had turned malignant after 8 years of indolence, manifested by tarry stools. The tumor was enucleated, and gastric GIST was diagnosed. The most significant histological finding was that the tumor comprised two clearly demarcated areas, one with less aggressive characteristics and the other with highly aggressive characteristics. The patient exhibited multiple liver metastases 24 months after surgery. Imatinib mesylate was not administered throughout the clinical course because it was not available for clinical use at that time. The patient followed an unfavorable clinical course and died of liver dysfunction 55 months after surgery. Autopsy was performed. By comparing the immunohistochemical profiles of primary and metastatic tumors, it was established that only the tumor cells with highly aggressive characteristics had metastasized.

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Uncommon Finding of a Gastrointestinal Stromal Tumor in a Patient with Hyperechoic Liver Lesions - Case Report

Acta Medica Marisiensis ◽

10.2478/amma-2018-0026 ◽

2018 ◽

Vol 64 (4) ◽

pp. 169-172

Author(s):

Adina Maria Roman ◽

Daniela Dobru ◽

Crina Fofiu ◽

Alina Boeriu

Keyword(s):

Case Report ◽

Gastrointestinal Stromal Tumor ◽

Kinase Inhibitors ◽

Narrow Band Imaging ◽

Submucosal Tumor ◽

Stromal Tumor ◽

Arterial Phase ◽

Gastric Gist ◽

Liver Lesions ◽

Diagnostic Challenge

AbstractIntroduction: Hyperechoic liver lesions identified by conventional ultrasonography are diverse in underlying pathology and most of the time require further investigations. Gastrointestinal stromal tumors (GIST) are rare neoplasms of the gastrointestinal tract which are uncommonly found in metastatic stages at first presentation.Case report: We present the case of a 51 years old woman with nonspecific symptoms in which conventional ultrasonography showed hyperechoic lesions in the right lobe of the liver with a diameter up to 40 mm. Esophagogastroduodenoscopy revealed a submucosal tumor on the small curvature of the stomach, on the anterior wall, with central ulceration, with normal narrow band imaging (NBI) mucosal pattern and negative gastric biopsy. Contrast enhanced ultrasonography was performed, describing multiple lesions with inhomogeneous enhancement in the arterial phase and rapid washout at the end of arterial phase. Endoscopic ultrasound with fine needle aspiration (EUS-FNA) biopsy examination was definitive for the final diagnosis of epithelioid gastric gastrointestinal stromal tumor. The patient was diagnosed with T2N0M1 epithelioid gastric GIST, stage IV, and is currently under treatment with tyrosine kinase inhibitors.Conclusions: GIST represent a diagnostic challenge in medical practice because of its size, unusual location in the submucosal layer and lack of symptoms. The role of EUS-FNA is of paramount importance in increasing the accuracy of diagnosis in the case of GIST. The particularity in our case consists of the unusual presentation with the lack of specific symptoms and signs associated with the presence of metastatic lesions at the moment of the diagnosis of GIST.

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Prediction of Coating Adhesion on Laser-Cleaned Metal Surfaces of Battery Cells Using Hyperspectral Imaging and Machine Learning

Coatings ◽

10.3390/coatings11111388 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1388

Author(s):

Johannes Maximilian Vater ◽

Florian Gruber ◽

Wulf Grählert ◽

Sebastian Schneider ◽

Alois Christian Knoll

Keyword(s):

Machine Learning ◽

Quality Control ◽

Hyperspectral Imaging ◽

Near Infrared ◽

Support Vector ◽

Electromagnetic Spectrum ◽

Coating Adhesion ◽

Cell Coating ◽

The Cross ◽

Battery Cells

Electric vehicles are shaping the future of the automotive industry. The traction battery is one of the most important components of electric cars. To ensure that the battery operates safely, it is essential to physically and electrically separate the cells facing each other. Coating a cell with varnish helps achieve this goal. Current studies use a destructive method on a sampling basis, the cross-cut test, to investigate the coating quality. In this paper, we present a fast, nondestructive and inline alternative based on hyperspectral imaging and artificial intelligence. Therefore, battery cells are measured with hyperspectral cameras in the visible and near-infrared (VNIR and NIR) parts of the electromagnetic spectrum before and after cleaning then coated and finally subjected to cross-cut test to estimate coating adhesion. During the cross-cut test, the cell coating is destroyed. This work aims to replace cross-cut tests with hyperspectral imaging (HSI) and machine learning to achieve continuous quality control, protect the environment, and save costs. Therefore, machine learning models (logistic regression, random forest, and support vector machines) are used to predict cross-cut test results based on hyperspectral data. We show that it is possible to predict with an accuracy of ~75% whether problems with coating adhesion will occur. Hyperspectral measurements in the near-infrared part of the spectrum yielded the best results. The results show that the method is suitable for automated quality control and process control in battery cell coating, but still needs to be improved to achieve higher accuracies.

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Preoperatively diagnosed gastric collision tumor with mixed adenocarcinoma and gastrointestinal stromal tumor: a case report and literature review

Clinical Journal of Gastroenterology ◽

10.1007/s12328-021-01343-4 ◽

2021 ◽

Author(s):

Kunihiko Matsuno ◽

Yoshikazu Kanazawa ◽

Daisuke Kakinuma ◽

Nobutoshi Hagiwara ◽

Fumihiko Ando ◽

...

Keyword(s):

Gastrointestinal Stromal Tumor ◽

Distal Gastrectomy ◽

Submucosal Tumor ◽

Smooth Muscle Actin ◽

Collision Tumor ◽

Stromal Tumor ◽

Lower Body ◽

Muscle Actin ◽

First Case ◽

S 100

AbstractReports of gastric collision tumors, comprising adenocarcinoma and gastrointestinal stromal tumor, are extremely rare. Here, we report the case of a 68-year-old male who was diagnosed with a lower-body, moderately differentiated, tubular-type adenocarcinoma and submucosal tumor and underwent an elective D2 distal gastrectomy. The tumor cells of the gastrointestinal stromal tumor were positive for H-caldesmon and CD117, weakly positive for smooth muscle actin and DOG-1, and negative for desmin, S-100 protein, CD31, and AE1/AE3. The tumor had grown into a mixed form of adenocarcinoma and gastrointestinal stromal tumor. Thus, we report the first case of a preoperatively diagnosed collision tumor in the stomach consisting of adenocarcinoma and gastrointestinal stromal tumor.

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NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽

10.3390/s21072503 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2503

Author(s):

Taro Suzuki ◽

Yoshiharu Amano

Keyword(s):

Machine Learning ◽

Satellite System ◽

Training Data ◽

Support Vector ◽

Positioning Errors ◽

Automated Method ◽

Global Navigation Satellite ◽

Better Than ◽

Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

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Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification

Sensors ◽

10.3390/s21113827 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3827

Author(s):

Gemma Urbanos ◽

Alberto Martín ◽

Guillermo Vázquez ◽

Marta Villanueva ◽

Manuel Villa ◽

...

Keyword(s):

Machine Learning ◽

Blood Vessel ◽

Hyperspectral Imaging ◽

Imaging Techniques ◽

Venous Blood ◽

Healthy Tissue ◽

Supervised Machine Learning ◽

Support Vector ◽

Arterial Blood

Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.

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Identification of peat type and humification by laboratory VNIR/SWIR hyperspectral imaging of peat profiles with focus on fen-bog transition in aapa mires

Plant and Soil ◽

10.1007/s11104-020-04775-y ◽

2020 ◽

Author(s):

L. Granlund ◽

M. Keinänen ◽

T. Tahvanainen

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Support Vector ◽

High Resolution Imaging ◽

Factors Affecting ◽

Vector Machines ◽

Resolution Imaging ◽

Shortwave Infrared ◽

Type Classification ◽

Wide Potential

Abstract Aims Hyperspectral imaging (HSI) has high potential for analysing peat cores, but methodologies are deficient. We aimed for robust peat type classification and humification estimation. We also explored other factors affecting peat spectral properties. Methods We used two laboratory setups: VNIR (visible to near-infrared) and SWIR (shortwave infrared) for high resolution imaging of intact peat profiles with fen-bog transitions. Peat types were classified with support vector machines, indices were developed for von Post estimation, and K-means clustering was used to analyse stratigraphic patterns in peat quality. With separate experiments, we studied spectral effects of drying and oxidation. Results Despite major effects, oxidation and water content did not impede robust HSI classification. The accuracy between Carex peat and Sphagnum peat in validation was 80% with VNIR and 81% with SWIR data. The spectral humification indices had accuracies of 82% with VNIR and 56%. Stratigraphic HSI patterns revealed that 36% of peat layer shifts were inclined by over 20 degrees. Spectral indices were used to extrapolate visualisations of element concentrations. Conclusions HSI provided reliable information of basic peat quality and was useful in visual mapping, that can guide sampling for other analyses. HSI can manage large amounts of samples to widen the scope of detailed analysis beyond single profiles and it has wide potential in peat research beyond the exploratory scope of this paper. We were able to confirm the capacity of HSI to reveal shifts of peat quality, connected to ecosystem-scale change.

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