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BACKGROUND/AIMS:  The endoscopic features of small-bowel gastrointestinal stromal tumors (GISTs) are not well defined. The objective of this study was to describe the endoscopic features of GISTs of the small intestine detected via single-balloon enteroscopy (SBE). MATERIALS AND METHODS:  Patients with surgically confirmed small intestinal GISTs from January 2014 to September 2022 were retrospectively analyzed. The hospital's electronic medical record system was used to retrieve the patients' data, including their demographics, clinical symptoms, hemoglobin on admission, endoscopic and computerized tomography findings, clinicopathological findings, and surgical management data. RESULTS:  In total, 46 GIST patients (23 men and 23 women) with overt bleeding were included, with a mean age of 52 years (23-80 years). The typical duration of the symptoms was 48 hours. Four patients (8.70%) had lesions in the duodenum, 32 (69.56%) had lesions in the jejunum, 8 (17.39%) had lesions in the ileum, and 2 (4.35%) had lesions around the junction of the jejunum and ileum. Out of the 46 patients, 27 underwent SBE, and GISTs were visualized in 25, while the lesions could not be visualized in the remaining 2. Submucosal round (n = 13), submucosal sessile (n = 8), and invasive/penetrating (n = 4) were among the endoscopic tumor features. Twenty patients exhibited submucosal protuberant lesions, with ulceration, vascular nodules/congestion, or erosion on the surface, and 5 patients presented ulcerative infiltrative lesions. The multiple logistic regression analysis indicated that the invasive/penetrating characteristics of GISTs under SBE evaluation are significantly correlated with the risk level of GIST malignancy (P < .05). CONCLUSION:  A variety of endoscopic characteristics could be observed during the preoperative SBE evaluation of small-intestine GISTs.

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BACKGROUND/AIM: Intraoperative identification of the cancer location is often difficult during robot-assisted surgery, especially in early stage cancers. This study aimed to investigate the feasibility and accuracy of a novel endoscopic clip emitting near-infrared (NIR) fluorescence during robot-assisted surgery for gastrointestinal cancer. PATIENTS AND METHODS: Preoperative placement of endoscopic marking clips equipped with NIR fluorescent resin was performed to determine the resection margins in six patients with gastrointestinal cancer. During robot-assisted surgery, a NIR fluorescence imaging system was used to detect the fluorescence. The evaluation examined whether fluorescence from the clips was visualized during robot-assisted surgery. RESULTS: The NIR fluorescent signals emitted from the clips were successfully detected in all six patients from the serosal surfaces, resulting in the quick and accurate identification of the resection line. There were no significant differences in age, sex, or body mass index between the patients in whom we could detect NIR fluorescence. CONCLUSION: This novel NIR fluorescent clip is a promising diagnostic tool for accurately detecting tumor locations during robot-assisted surgery for gastrointestinal cancer.

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Objective.To develop and validate a dose-of-the-day (DOTD) treatment plan verification procedure for liver and pancreas cancer patients treated with an magnetic resonance (MR)-Linac system.Approach.DOTD was implemented as an automated process that uses 3D datasets collected during treatment delivery. Particularly, the DOTD pipeline's input included the adapt-to-shape (ATS) plan-i.e. 3D-MR dataset acquired at beginning of online session, anatomical contours, dose distribution-and 3D-MR dataset acquired during beam-on (BON). The DOTD automated analysis included (a) ATS-to-BON image intensity-based deformable image registration (DIR), (b) ATS-to-BON contours mapping via DIR, (c) BON-to-ATS contours copying through rigid registration, (d) determining ATS-to-BON dosimetric differences, and (e) PDF report generation. The DIR process was validated by two expert reviewers. ATS-plans were recomputed on BON datasets to assess dose differences. DOTD analysis was performed retrospectively for 75 treatment fractions (12-liver and 5-pancreas patients).Main results.The accuracy of DOTD process relied on DIR and mapped contours quality. Most DIR-generated contours (99.6%) were clinically acceptable. DICE correlated with depreciation of DIR-based region of interest mapping process. The ATS-BON plan difference was found negligible (<1%). The duodenum and large bowel exhibited highest variations, 24% and 39% from fractional values, for 5-fraction liver and pancreas. For liver 1-fraction, a 62% variation was observed for duodenum.Significance.The DOTD methodology provides an automated approach to quantify 3D dosimetric differences between online plans and their delivery. This analysis offers promise as a valuable tool for plan quality assessment and decision-making in the verification stage of the online workflow.

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OBJECTIVES: Recently, artificial intelligence (AI) has been applied to clinical diagnosis. Although AI has already been developed for gastrointestinal (GI) tract endoscopy, few studies have applied AI to endoscopic ultrasound (EUS) images. In this study, we used a computer-assisted diagnosis (CAD) system with deep learning analysis of EUS images (EUS-CAD) and assessed its ability to differentiate GI stromal tumors (GISTs) from other mesenchymal tumors and their risk classification performance. MATERIALS AND METHODS: A total of 101 pathologically confirmed cases of subepithelial lesions (SELs) arising from the muscularis propria layer, including 69 GISTs, 17 leiomyomas and 15 schwannomas, were examined. A total of 3283 EUS images were used for training and five-fold-cross-validation, and 827 images were independently tested for diagnosing GISTs. For the risk classification of 69 GISTs, including very-low-, low-, intermediate- and high-risk GISTs, 2,784 EUS images were used for training and three-fold-cross-validation. RESULTS: For the differential diagnostic performance of GIST among all SELs, the accuracy, sensitivity, specificity and area under the receiver operating characteristic (ROC) curve were 80.4%, 82.9%, 75.3% and 0.865, respectively, whereas those for intermediate- and high-risk GISTs were 71.8%, 70.2%, 72.0% and 0.771, respectively. CONCLUSIONS: The EUS-CAD system showed a good diagnostic yield in differentiating GISTs from other mesenchymal tumors and successfully demonstrated the GIST risk classification feasibility. This system can determine whether treatment is necessary based on EUS imaging alone without the need for additional invasive examinations.

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PURPOSE: To develop a deep neural network system for the automatic segmentation and risk stratification prediction of gastrointestinal stromal tumors (GISTs). METHODS: A total of 980 ultrasound (US) images from 245 GIST patients were retrospectively collected. These images were randomly divided (6:2:2) into a training set, a validation set, and an internal test set. Additionally, 188 US images from 47 prospective GIST patients were collected to evaluate the segmentation and diagnostic performance of the model. Five deep learning-based segmentation networks, namely, UNet, FCN, DeepLabV3+, Swin Transformer, and SegNeXt, were employed, along with the ResNet 18 classification network, to select the most suitable network combination. The performance of the segmentation models was evaluated using metrics such as the intersection over union (IoU), Dice similarity coefficient (DSC), recall, and precision. The classification performance was assessed based on accuracy and the area under the receiver operating characteristic curve (AUROC). RESULTS: Among the compared models, SegNeXt-ResNet18 exhibited the best segmentation and classification performance. On the internal test set, the proposed model achieved IoU, DSC, precision, and recall values of 82.1, 90.2, 91.7, and 88.8%, respectively. The accuracy and AUC for GIST risk prediction were 87.4 and 92.0%, respectively. On the external test set, the segmentation models exhibited IoU, DSC, precision, and recall values of 81.0, 89.5, 92.8, and 86.4%, respectively. The accuracy and AUC for GIST risk prediction were 86.7 and 92.5%, respectively. CONCLUSION: This two-stage SegNeXt-ResNet18 model achieves automatic segmentation and risk stratification prediction for GISTs and demonstrates excellent segmentation and classification performance.

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BACKGROUND: The cutoff value for stereomicroscopic on-site evaluation (SOSE) in endoscopic ultrasound-guided tissue acquisition (EUS-TA) has high diagnostic sensitivity when a Franseen needle is employed for upper gastrointestinal subepithelial lesions (SELs) (stereomicroscopically visible white core [SVWC] ≥ 4 mm). AIM: We aimed to determine whether high diagnostic sensitivity could be obtained when EUS-TA was performed using a Fork-tip needle. METHODS: Twenty-one patients were prospectively registered. Patients underwent EUS-TA using a Fork-tip needle for upper gastrointestinal SELs at Kitasato University Hospital between January and November 2022. Punctures were made twice using the needle, and SOSE was conducted for each specimen. Blood and physical examination were performed to assess adverse events. Pathological diagnosis was made using hematoxylin and eosin-stained sections and immunohistochemical staining. Statistical comparisons were completed using Fisher's exact tests. RESULTS: The diagnostic rate of EUS-TA was 100% (21/21 cases). The final diagnosis was gastrointestinal stromal tumor in 17 (81.0%) and leiomyoma in 4 (19.0%) patients. SOSE was conducted on all 42 punctures, and the tissue sampling rate was 100% (42/42 punctures). Specimens with SVWC ≥ 4 mm were collected in 97.6% punctures (41/42 punctures) and the diagnostic sensitivity for these specimens was 100% (41/41 punctures), which is significantly higher (p < 0.0238) compared to the absence of cutoff value (diagnostic sensitivity of 0%). No EUS-TA-related adverse events occurred. CONCLUSIONS: EUS-TA combined with SOSE for upper gastrointestinal SEL using a fork-tip needle had a high diagnostic rate, and the cutoff value of SVWC ≥ 4 mm had high diagnostic sensitivity.

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Gastrointestinal (GI) cancer is leading general tumour in the Gastrointestinal tract, which is fourth significant reason of tumour death in men and women. The common cure for GI cancer is radiation treatment, which contains directing a high-energy X-ray beam onto the tumor while avoiding healthy organs. To provide high dosages of X-rays, a system needs for accurately segmenting the GI tract organs. The study presents a UMobileNetV2 model for semantic segmentation of small and large intestine and stomach in MRI images of the GI tract. The model uses MobileNetV2 as an encoder in the contraction path and UNet layers as a decoder in the expansion path. The UW-Madison database, which contains MRI scans from 85 patients and 38,496 images, is used for evaluation. This automated technology has the capability to enhance the pace of cancer therapy by aiding the radio oncologist in the process of segmenting the organs of the GI tract. The UMobileNetV2 model is compared to three transfer learning models: Xception, ResNet 101, and NASNet mobile, which are used as encoders in UNet architecture. The model is analyzed using three distinct optimizers, i.e., Adam, RMS, and SGD. The UMobileNetV2 model with the combination of Adam optimizer outperforms all other transfer learning models. It obtains a dice coefficient of 0.8984, an IoU of 0.8697, and a validation loss of 0.1310, proving its ability to reliably segment the stomach and intestines in MRI images of gastrointestinal cancer patients.

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There is a diverse group of non-gastrointestinal stromal tumor (GIST), mesenchymal neoplasms of the gastrointestinal (GI) tract that demonstrate characteristic pathology and histogenesis as well as variable imaging findings and biological behavior. Recent advancements in tumor genetics have unveiled specific abnormalities associated with certain tumors, influencing their molecular pathogenesis, biology, response to treatment, and prognosis. Notably, giant fibrovascular polyps of the esophagus, identified through MDM2 gene amplifications, are now classified as liposarcomas. Some tumors exhibit distinctive patterns of disease distribution. Glomus tumors and plexiform fibromyxomas exhibit a pronounced affinity for the gastric antrum. In contrast, smooth muscle tumors within the GI tract are predominantly found in the esophagus and colorectum, surpassing the incidence of GISTs in these locations. Surgical resection suffices for symptomatic benign tumors; multimodality treatment may be necessary for frank sarcomas. This article aims to elucidate the cross-sectional imaging findings associated with a wide spectrum of these tumors, providing insights that align with their histopathological features.

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CLINICAL/METHODICAL ISSUE: Neuroendocrine tumors (NET) represent a heterogeneous group of rare tumors that predominantly arise in the gastrointestinal tract. At the time of initial diagnosis, the NET has already spread locoregionally in about half of the patients, and 27% of patients have already developed distant metastases. Since this plays a crucial role in therapy planning, accurate diagnostic imaging is important. STANDARD RADIOLOGICAL METHODS: Due to its high temporal and spatial resolution (multiphasic including arterial phase), computed tomography (CT) plays a decisive role in primary staging and follow-up care, while magnetic resonance imaging (MRI) with its excellent soft tissue contrast offers advantages in the assessment of parenchymal organs in the upper abdomen. METHODICAL INNOVATIONS: Somatostatin receptor (SSR) positron emission tomography (PET) provides additional functional information that not only helps to detect the primary tumor and distant metastases, but also has a significant influence on therapeutic management in a theranostic approach. PERFORMANCE: Hybrid imaging using SSR-PET/CT has proven to be particularly effective in the detection of NET. Compared to conventional imaging, it provides additional information in 68% of patients, which has a significant impact on clinical management. ACHIEVEMENTS: Imaging of NET requires the combined use of various methods such as ultrasound, CT, MRI, and PET/CT to enable accurate diagnosis and effective treatment planning. PRACTICAL RECOMMENDATIONS: SSR-PET/CT is a valuable tool for the accurate staging of neuroendocrine tumors of the gastrointestinal tract, especially with small metastases, while MRI with hepatocyte-specific contrast agent and diffusion-weighted imaging is useful for the specific assessment of liver metastases.

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BACKGROUND: The metastasis of primary breast invasive lobular carcinoma to the gastrointestinal tract and skin is a rare phenomenon, with the simultaneous occurrence of both transfers being more uncommon. CASE PRESENTATION: This article reports a case of a patient with hormone receptor-positive, HER2-negative breast invasive lobular carcinoma with gastrointestinal tract and skin metastases. The patient was assessed by a second-look ultrasound and diagnosed by subsequent ultrasound-guided needle biopsy. Following endocrine therapy, a favorable effect was observed, with significant regression of the primary breast lesion, cutaneous metastases, and gastrointestinal metastases. CONCLUSION: Patients with breast invasive lobular carcinoma should be alert to the possibility of breast cancer metastasis, even if there are no obvious symptoms or signs, when they encounter rapidly progressive cutaneous nodules or plaques, or if they possess gastrointestinal abnormalities. For patients with negative breast ultrasonography for the first time, after combining mammography, Contrast-enhanced Spectral Mammography (CESM) or Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) examinations, if breast cancer is highly suspected, second-look ultrasound is particularly crucial at this juncture, which is the key prerequisite for breast needle biopsy and obtaining the gold standard of pathology.

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68Ga-labeled nanobody (68Ga-NC-BCH) is a single-domain antibody-based PET imaging agent. We conducted a first-in-humans study of 68Ga-NC-BCH for PET to determine its in vivo biodistribution, metabolism, radiation dosimetry, safety, and potential for quantifying claudin-18 isoform 2 (CLDN18.2) expression in gastrointestinal cancer patients. Methods: Initially, we synthesized the probe 68Ga-NC-BCH and performed preclinical evaluations on human gastric adenocarcinoma cell lines and xenograft mouse models. Next, we performed a translational study with a pilot cohort of patients with advanced gastrointestinal cancer on a total-body PET/CT scanner. Radiopharmaceutical biodistribution, radiation dosimetry, and the relationship between tumor uptake and CLDN18.2 expression were evaluated. Results: 68Ga-NC-BCH was stably prepared and demonstrated good radiochemical properties. According to preclinical evaluation,68Ga-NC-BCH exhibited rapid blood clearance, high affinity for CLDN18.2, and high specific uptake in CLDN18.2-positive cells and xenograft mouse models. 68Ga-NC-BCH displayed high uptake in the stomach and kidney and slight uptake in the pancreas. Compared with 18F-FDG, 68Ga-NC-BCH showed significant differences in uptake in lesions with different levels of CLDN18.2 expression. Conclusion: A clear correlation was detected between PET SUV and CLDN18.2 expression, suggesting that 68Ga-NC-BCH PET could be used as a companion diagnostic tool for optimizing treatments that target CLDN18.2 in tumors.

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As the management of gastrointestinal malignancy has evolved, tumor response assessment has expanded from size-based assessments to those that include tumor enhancement, in addition to functional data such as those derived from PET and diffusion-weighted imaging. Accurate interpretation of tumor response therefore requires knowledge of imaging modalities used in gastrointestinal malignancy, anticancer therapies, and tumor biology. Targeted therapies such as immunotherapy pose additional considerations due to unique imaging response patterns and drug toxicity; as a consequence, immunotherapy response criteria have been developed. Some gastrointestinal malignancies require assessment with tumor-specific criteria when assessing response, often to guide clinical management (such as watchful waiting in rectal cancer or suitability for surgery in pancreatic cancer). Moreover, anatomic measurements can underestimate therapeutic response when applied to molecular-targeted therapies or locoregional therapies in hypervascular malignancies such as hepatocellular carcinoma. In these cases, responding tumors may exhibit morphologic changes including cystic degeneration, necrosis, and hemorrhage, often without significant reduction in size. Awareness of pitfalls when interpreting gastrointestinal tumor response is required to correctly interpret response assessment imaging and guide appropriate oncologic management. Data-driven image analyses such as radiomics have been investigated in a variety of gastrointestinal tumors, such as identifying those more likely to respond to therapy or recur, with the aim of delivering precision medicine. Multimedia-enhanced radiology reports can facilitate communication of gastrointestinal tumor response by automatically embedding response categories, key data, and representative images. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

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PURPOSE: Gastrointestinal tract (GIT) tumors in children are rare and there is a scarcity of data on their imaging features. The purpose of this study was to determine thefrequency of various GIT tumor types in children and to identify key imaging characteristics. METHODS: This retrospective, single-center study was approved by the local ethics committee. Children with histologically proven GIT tumours (malignantand benign) who had imaging available on the institutional PACS between May 1, 2000 and Dec 31, 2019 were included. Demographic data and available imaging was reviewed by two blinded radiologists. RESULTS: In total, 90 children (45 male, mean age 9.3 ± 4.3 years) with GIT tumours were included. The final diagnoses included polyps (n = 28), lymphomas/PTLD (n = 27), neuroendocrine tumours (n = 16), adenocarcinoma (n = 6), adenoma (n = 5), gastrointestinal stromal tumor (GIST) (n = 3), inflammatory myofibroblastic tumours (n = 2) and lastly leiomyoblastoma, leiomyoma and lipoma (1 each). All GIT segments were affected, but overall the small and large bowel had most lesions. Eighty-one percent children had a single lesion while remaining 19 % had multiple lesions. The neoplastic process manifested as intra-luminal lesion (58 %) or wall thickening (42 %) on imaging. Multiple cystic areas and vascular pedicle for polyps; and hypoechogenecity of the mass or wall thickening and aneurysmal dilatation for lymphomas, were the characteristic imaging features. None of the neuroendocrine tumours affecting appendix were seen on pre-resection imaging. CONCLUSIONS: Variety of benign and malignant tumors are seen throughout the childhood. Polyps, lymphomas and appendiceal neuroendocrine tumors are common lesions. Characteristic imaging features of juvenile polyps and lymphomas on ultrasound may help narrowing the differentials, and guide further work up.

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PURPOSE: To evaluate and compare the diagnostic performances of whole-lesion iodine map (IM) histogram analysis and single-slice IM measurement in the risk classification of gastrointestinal stromal tumors (GISTs). METHODS: Thirty-seven patients with GISTs, including 19 with low malignant underlying GISTs (LG-GISTs) and 18 with high malignant underlying GISTs (HG-GISTs), were evaluated with dual-energy computed tomography (DECT). Whole-lesion IM histogram parameters (mean; median; minimum; maximum; standard deviation; variance; 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile; kurtosis, skewness, and entropy) were computed for each lesion. In other sessions, iodine concentrations (ICs) were derived from the IM by placing regions of interest (ROIs) on the tumor slices and normalizing them to the iodine concentration in the aorta. Both quantitative analyses were performed on the venous phase images. The diagnostic accuracies of the two methods were assessed and compared. RESULTS: The minimum, maximum, 1st, 10th, and 25th percentile of the whole-lesion IM histogram and the IC and normalized IC (NIC) of the single-slice IC measurement significantly differed between LG- and HG-GISTs (p < 0.001 - p = 0.042). The minimum value in the histogram analysis (AUC = 0.844) and the NIC in the single-slice measurement analysis (AUC = 0.886) showed the best diagnostic performances. The NIC of single-slice measurements had a diagnostic performance similar to that of the whole-lesion IM histogram analysis (p = 0.618). CONCLUSIONS: Both whole-lesion IM histogram analysis and single-slice IC measurement can differentiate LG-GISTs and HG-GISTs with similar diagnostic performances.

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Learning better representations is essential in medical image analysis for computer-aided diagnosis. However, learning discriminative semantic features is a major challenge due to the lack of large-scale well-annotated datasets. Thus, how can we learn a well-structured categorizable embedding space in limited-scale and unlabeled datasets? In this paper, we proposed a novel clustering-guided twin-contrastive learning framework (CTCL) that learns the discriminative representations of probe-based confocal laser endomicroscopy (pCLE) images for gastrointestinal (GI) tumor classification. Compared with traditional contrastive learning, in which only two randomly augmented views of the same instance are considered, the proposed CTCL aligns more semantically related and class-consistent samples by clustering, which improved intra-class tightness and inter-class variability to produce more informative representations. Furthermore, based on the inherent properties of CLE (geometric invariance and intrinsic noise), we proposed to regard CLE images with any angle rotation and CLE images with different noises as the same instance, respectively, for increased variability and diversity of samples. By optimizing CTCL in an end-to-end expectation-maximization framework, comprehensive experimental results demonstrated that CTCL-based visual representations achieved competitive performance on each downstream task as well as more robustness and transferability compared with existing state-of-the-art SSL and supervised methods. Notably, CTCL achieved 75.60%/78.45% and 64.12%/77.37% top-1 accuracy on the linear evaluation protocol and few-shot classification downstream tasks, respectively, which outperformed the previous best results by 1.27%/1.63% and 0.5%/3%, respectively. The proposed method holds great potential to assist pathologists in achieving an automated, fast, and high-precision diagnosis of GI tumors and accurately determining different stages of tumor development based on CLE images.

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