The value of dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting EGFR mutations.

OBJECTIVES: 18F-fluorodeoxyglucose (FDG) PET/CT has been widely used for the differential diagnosis of cancer. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may make it difficult to differentiate between benign and malignant lesions. It is crucial to find reliable quantitative metabolic parameters to further support the diagnosis. This study aims to evaluate the value of the quantitative metabolic parameters derived from dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting epidermal growth factor receptor (EGFR) mutation status. METHODS: We included 147 patients with lung lesions to perform FDG PET/CT dynamic plus static imaging with informed consent. Based on the results of the postoperative pathology, the patients were divided into benign/malignant groups, adenocarcinoma (AC)/squamous carcinoma (SCC) groups, and EGFR-positive (EGFR+)/EGFR-negative (EGFR-) groups. Quantitative parameters including K1, k2, k3, and Ki of each lesion were obtained by applying the irreversible two-tissue compartmental modeling using an in-house Matlab software. The SUV analysis was performed based on conventional static scan data. Differences in each metabolic parameter among the group were analyzed. Wilcoxon rank-sum test, independent-samples T-test, and receiver-operating characteristic (ROC) analysis were performed to compare the diagnostic effects among the differentiated groups. P < 0.05 were considered statistically significant for all statistical tests. RESULTS: In the malignant group (N = 124), the SUVmax, k2, k3, and Ki were higher than the benign group (N = 23), and all had-better performance in the differential diagnosis (P < 0.05, respectively). In the AC group (N = 88), the SUVmax, k3, and Ki were lower than in the SCC group, and such differences were statistically significant (P < 0.05, respectively). For ROC analysis, Ki with cut-off value of 0.0250 ml/g/min has better diagnostic specificity than SUVmax (AUC = 0.999 vs. 0.70). In AC group, 48 patients further underwent EGFR testing. In the EGFR (+) group (N = 31), the average Ki (0.0279 ± 0.0153 ml/g/min) was lower than EGFR (-) group (N = 17, 0.0405 ± 0.0199 ml/g/min), and the difference was significant (P < 0.05). However, SUVmax and k3 did not show such a difference between EGFR (+) and EGFR (-) groups (P>0.05, respectively). For ROC analysis, the Ki had a cut-off value of 0.0350 ml/g/min when predicting EGFR status, with a sensitivity of 0.710, a specificity of 0.588, and an AUC of 0.674 [0.523-0.802]. CONCLUSION: Although both techniques were specific, Ki had a greater specificity than SUVmax when the cut-off value was set at 0.0250 ml/g/min for the differential diagnosis of lung cancer. At a cut-off value of 0.0350 ml/g/min, there was a 0.710 sensitivity for EGFR status prediction. If EGFR testing is not available for a patient, dynamic imaging could be a valuable non-invasive screening method.

An isolated vaginal metastasis from rectal cancer: a case report.

INTRODUCTION: Vaginal metastasis from colorectal cancer is a rare occurrence, typically associated with other metastatic lesions. Isolated metastasis is exceedingly uncommon, with only a few cases documented in the literature. Vaginal involvement in colorectal cancer primarily results from direct contiguous spread from the primary tumor. CASE PRESENTATION: We present the case of a 70-year-old African woman diagnosed with adenocarcinoma of the middle rectum. She underwent chemotherapy, radiotherapy, and subsequent anterior resection. After 2 months, an isolated metastasis of rectal cancer was identified in the lower third of the left vaginal wall, confirmed by biopsy. Colonoscopy ruled out colorectal recurrence. Thoraco-abdominal computed tomography scan showed no distant metastases. The patient underwent abdominoperineal resection, removing the lateral and posterior vaginal wall with free macroscopic margins and a definitive colostomy. The final histopathological analysis confirmed the diagnosis of moderately differentiated adenocarcinoma of the vagina, measuring 5 × 4.5 cm. The rectal wall was extrinsically invaded by the tumor down to the muscularis propria while respecting the rectal mucosa. Resection margins were negative. The patient was discharged 1 week postoperation with no complications. Adjuvant chemotherapy was indicated, and the patient is currently tolerating the treatment well. CONCLUSION: Vaginal metastases from colorectal cancer are extremely rare. A vigilant gynecological examination is recommended during the follow-up of colorectal cancer patients. Diagnosis can be challenging, especially if the metastatic lesion is small and asymptomatic, even after standard radiological examination. Surgical resection followed by chemotherapy is a valid option for patients with early isolated metastases.

Oil blotting paper for formalin fixation increases endoscopic ultrasound-guided tissue acquisition-collected sample volumes on glass slides.

OBJECTIVES: Endoscopic ultrasound-guided tissue acquisition (EUS-TA) is used for pathological diagnosis and obtaining samples for molecular testing, facilitating the initiation of targeted therapies in patients with pancreatic cancer. However, samples obtained via EUS-TA are often insufficient, requiring more efforts to improve sampling adequacy for molecular testing. Therefore, this study investigated the use of oil blotting paper for formalin fixation of samples obtained via EUS-TA. METHODS: This prospective study enrolled 42 patients who underwent EUS-TA for pancreatic cancer between September 2020 and February 2022 at the Osaka International Cancer Institute. After a portion of each sample obtained via EUS-TA was separated for routine histological evaluation, the residual samples were divided into filter paper and oil blotting paper groups for analysis. Accordingly, filter paper and oil blotting paper were used for the formalin fixation process. The total tissue, nuclear, and cytoplasm areas of each sample were quantitatively evaluated using virtual slides, and the specimen volume and histological diagnosis of each sample were evaluated by an expert pathologist. RESULTS: All cases were cytologically diagnosed as adenocarcinoma. The area ratios of the total tissue, nuclear, and cytoplasmic portions were significantly larger in the oil blotting paper group than in the filter paper group. The frequency of cases with large amount of tumor cells was significantly higher in the oil blotting paper group (33.3%) than in the filter paper group (11.9%) (p = 0.035). CONCLUSIONS: Oil blotting paper can increase the sample volume obtained via EUS-TA on glass slides and improve sampling adequacy for molecular testing.

Krukenburg tumour: Role of 18F-FDG PET/CT in identifying the primary site.

Krukenberg tumours are a rare form of metastatic tumours of the ovary. They primary site is usually the gastro-intestinal system with the most common being gastric cancer. We present the case of a 35-year-old female coming in with a large pelvi-abdominal mass for investigation. This pelvic mass showed mild to moderate metabolic activity. 18F-FDG PET-CT was able to identify the primary gastric carcinoma. Subsequent histopathology confirmed this to be gastric adenocarcinoma with metastases to the ovary.

Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models.

Background Preoperative discrimination of preinvasive, minimally invasive, and invasive adenocarcinoma at CT informs clinical management decisions but may be challenging for classifying pure ground-glass nodules (pGGNs). Deep learning (DL) may improve ternary classification. Purpose To determine whether a strategy that includes an adjudication approach can enhance the performance of DL ternary classification models in predicting the invasiveness of adenocarcinoma at chest CT and maintain performance in classifying pGGNs. Materials and Methods In this retrospective study, six ternary models for classifying preinvasive, minimally invasive, and invasive adenocarcinoma were developed using a multicenter data set of lung nodules. The DL-based models were progressively modified through framework optimization, joint learning, and an adjudication strategy (simulating a multireader approach to resolving discordant nodule classifications), integrating two binary classification models with a ternary classification model to resolve discordant classifications sequentially. The six ternary models were then tested on an external data set of pGGNs imaged between December 2019 and January 2021. Diagnostic performance including accuracy, specificity, and sensitivity was assessed. The χ2 test was used to compare model performance in different subgroups stratified by clinical confounders. Results A total of 4929 nodules from 4483 patients (mean age, 50.1 years ± 9.5 [SD]; 2806 female) were divided into training (n = 3384), validation (n = 579), and internal (n = 966) test sets. A total of 361 pGGNs from 281 patients (mean age, 55.2 years ± 11.1 [SD]; 186 female) formed the external test set. The proposed strategy improved DL model performance in external testing (P < .001). For classifying minimally invasive adenocarcinoma, the accuracy was 85% and 79%, sensitivity was 75% and 63%, and specificity was 89% and 85% for the model with adjudication (model 6) and the model without (model 3), respectively. Model 6 showed a relatively narrow range (maximum minus minimum) across diagnostic indexes (accuracy, 1.7%; sensitivity, 7.3%; specificity, 0.9%) compared with the other models (accuracy, 0.6%-10.8%; sensitivity, 14%-39.1%; specificity, 5.5%-17.9%). Conclusion Combining framework optimization, joint learning, and an adjudication approach improved DL classification of adenocarcinoma invasiveness at chest CT. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Sohn and Fields in this issue.

Adenocarcinoma originating in the anterior mediastinum diagnosed by endobronchial ultrasound-guided transbronchial cryobiopsy: a case report.

BACKGROUND: Endobronchial ultrasound-guided transbronchial cryobiopsy (EBUS-cryobiopsy) is advantageous for collecting larger specimens with minimal crushing; however, it has not been widely used for mediastinal tumors. CASE PRESENTATION: A 73-year-old woman with a history of left breast cancer underwent surgery followed by radiotherapy. Computed tomography showed a mass in the anterior mediastinum that was in extensive contact with the sternum on the ventral side and partly with the trachea on the dorsal side. Two computed tomography-guided needle biopsies (CTNBs) were performed on the mass; however, a definitive diagnosis was not made because of severe crush artifacts. Subsequently, we performed EBUS-cryobiopsy and safely obtained sufficient specimen volume with minimal crushing. The histopathological diagnosis was adenocarcinoma, with immunobiological features distinct from those of previous breast cancers. Her overall diagnosis was a rare tumor originating in the anterior mediastinum. CONCLUSIONS: EBUS-cryobiopsy can be safely performed in narrow areas surrounded by major blood vessels, and the obtained specimens may be superior to CTNBs for histopathological diagnosis.

Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using Clinical-Metabolic characteristics and 18F-FDG PET/CT radiomics.

Noninvasive differentiation between the squamous cell carcinoma (SCC) and adenocarcinoma (ADC) subtypes of non-small cell lung cancer (NSCLC) could benefit patients who are unsuitable for invasive diagnostic procedures. Therefore, this study evaluates the predictive performance of a PET/CT-based radiomics model. It aims to distinguish between the histological subtypes of lung adenocarcinoma and squamous cell carcinoma, employing four different machine learning techniques. A total of 255 Non-Small Cell Lung Cancer (NSCLC) patients were retrospectively analyzed and randomly divided into the training (n = 177) and validation (n = 78) sets, respectively. Radiomics features were extracted, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was employed for feature selection. Subsequently, models were constructed using four distinct machine learning techniques, with the top-performing algorithm determined by evaluating metrics such as accuracy, sensitivity, specificity, and the area under the curve (AUC). The efficacy of the various models was appraised and compared using the DeLong test. A nomogram was developed based on the model with the best predictive efficiency and clinical utility, and it was validated using calibration curves. Results indicated that the logistic regression classifier had better predictive power in the validation cohort of the radiomic model. The combined model (AUC 0.870) exhibited superior predictive power compared to the clinical model (AUC 0.848) and the radiomics model (AUC 0.774). In this study, we discovered that the combined model, refined by the logistic regression classifier, exhibited the most effective performance in classifying the histological subtypes of NSCLC.

Correlation between Radiological Characteristics, PET-CT and Histological Subtypes of Primary Lung Adenocarcinoma-A 102 Case Series Analysis.

Background and Objectives: Lung cancer is the second most common form of cancer in the world for both men and women as well as the most common cause of cancer-related deaths worldwide. The aim of this study is to summarize the radiological characteristics between primary lung adenocarcinoma subtypes and to correlate them with FDG uptake on PET-CT. Materials and Methods: This retrospective study included 102 patients with pathohistologically confirmed lung adenocarcinoma. A PET-CT examination was performed on some of the patients and the values of SUVmax were also correlated with the histological and morphological characteristics of the masses in the lungs. Results: The results of this analysis showed that the mean size of AIS-MIA (adenocarcinoma in situ and minimally invasive adenocarcinoma) cancer was significantly lower than for all other cancer types, while the mean size of the acinar cancer was smaller than in the solid type of cancer. Metastases were significantly more frequent in solid adenocarcinoma than in acinar, lepidic, and AIS-MIA cancer subtypes. The maximum standardized FDG uptake was significantly lower in AIS-MIA than in all other cancer types and in the acinar predominant subtype compared to solid cancer. Papillary predominant adenocarcinoma had higher odds of developing contralateral lymph node involvement compared to other types. Solid adenocarcinoma was associated with higher odds of having metastases and with higher SUVmax. AIS-MIA was associated with lower odds of one unit increase in tumor size and ipsilateral lymph node involvement. Conclusions: The correlation between histopathological and radiological findings is crucial for accurate diagnosis and staging. By integrating both sets of data, clinicians can enhance diagnostic accuracy and determine the optimal treatment plan.

Short- and long-term outcomes after distal pancreatectomy with radiologic infiltration of splenic vessels for pancreatic ductal adenocarcinoma.

BACKGROUND: The effect of radiologic splenic vessels involvement (RSVI) on the survival of patients with pancreatic adenocarcinoma (PAC) located in the body and tail of the pancreas is controversial, and its influence on postoperative morbidity after distal pancreatectomy (DP) is unknown. This study aimed to determine the influence of RSVI on postoperative complications, overall survival (OS), and disease-free survival (DFS) in patients undergoing DP for PAC. METHODS: A multicenter retrospective study of DP was conducted at 7 hepatopancreatobiliary units between January 2008 and December 2018. Patients were classified according to the presence of RSVI. A Clavien-Dindo grade of >II was considered to represent a major complication. RESULTS: A total of 95 patients were included in the analysis. Moreover, 47 patients had vascular infiltration: 4 had arterial involvement, 10 had venous involvement, and 33 had both arterial and venous involvements. The rates of major complications were 20.8% in patients without RSVI, 40.0% in those with venous RSVI, 25.0% in those with arterial RSVI, and 30.3% in those with both arterial and venous RSVIs (P = .024). The DFS rates at 3 years were 56% in the group without RSVI, 50% in the group with arterial RSVI, and 16% in the group with both arterial and venous RSVIs (P = .003). The OS rates at 3 years were 66% in the group without RSVI, 50% in the group with arterial RSVI, and 29% in the group with both arterial and venous RSVIs (P < .0001). CONCLUSION: RSVI increased the major complication rates after DP and reduced the OS and DFS. Therefore, it may be a useful prognostic marker in patients with PAC scheduled to undergo DP and may help to select patients likely to benefit from neoadjuvant treatment.

Mean computed tomography value to predict spread through air spaces in clinical N0 lung adenocarcinoma.

BACKGROUND: The aim of this study was to assess the ability of radiologic factors such as mean computed tomography (mCT) value, consolidation/tumor ratio (C/T ratio), solid tumor size, and the maximum standardized uptake (SUVmax) value by F-18 fluorodeoxyglucose positron emission tomography to predict the presence of spread through air spaces (STAS) of lung adenocarcinoma. METHODS: A retrospective study was conducted on 118 patients those diagnosed with clinically without lymph node metastasis and having a pathological diagnosis of adenocarcinoma after undergoing surgery. Receiver operating characteristics (ROC) analysis was used to assess the ability to use mCT value, C/T ratio, tumor size, and SUVmax value to predict STAS. Univariate and multiple logistic regression analyses were performed to determine the independent variables for the prediction of STAS. RESULTS: Forty-one lesions (34.7%) were positive for STAS and 77 lesions were negative for STAS. The STAS positive group was strongly associated with a high mCT value, high C/T ratio, large solid tumor size, large tumor size and high SUVmax value. The mCT values were - 324.9 ± 19.3 HU for STAS negative group and - 173.0 ± 26.3 HU for STAS positive group (p < 0.0001). The ROC area under the curve of the mCT value was the highest (0.738), followed by SUVmax value (0.720), C/T ratio (0.665), solid tumor size (0.649). Multiple logistic regression analyses using the preoperatively determined variables revealed that mCT value (p = 0.015) was independent predictive factors of predicting STAS. The maximum sensitivity and specificity were obtained at a cutoff value of - 251.8 HU. CONCLUSIONS: The evaluation of mCT value has a possibility to predict STAS and may potentially contribute to the selection of suitable treatment strategies.

[Ascending colon cancer with submucosal tumor-like morphology arising near ileal valve lipohyperplasia:a case report].

An 89-year-old man was diagnosed with a submucosal tumor suspected to be a lipoma and was followed up for 6 years. The patient was admitted to the hospital because of increased tumor size and morphological changes despite negative bioptic findings. The lesion was diagnosed as an advanced adenocarcinoma of the ascending colon (cT3N0M0, cStage IIa). Laparoscopic-assisted right hemicolectomy with D3 lymph node dissection was performed. Pathological diagnosis of a surgically resected specimen revealed adenocarcinoma with lipohyperplasia (pT3N2aM0, pStage IIIb). Reports of colon cancer accompanied by colonic lipomas or lipohyperplasia are limited. This case showed an interesting submucosal tumor-like morphology because the cancer developed at the base of the lipohyperplasia and grew and spread below it.

Association Between Body Composition and Survival in Patients With Gastroesophageal Adenocarcinoma: An Automated Deep Learning Approach.

PURPOSE: Body composition (BC) may play a role in outcome prognostication in patients with gastroesophageal adenocarcinoma (GEAC). Artificial intelligence provides new possibilities to opportunistically quantify BC from computed tomography (CT) scans. We developed a deep learning (DL) model for fully automatic BC quantification on routine staging CTs and determined its prognostic role in a clinical cohort of patients with GEAC. MATERIALS AND METHODS: We developed and tested a DL model to quantify BC measures defined as subcutaneous and visceral adipose tissue (VAT) and skeletal muscle on routine CT and investigated their prognostic value in a cohort of patients with GEAC using baseline, 3-6-month, and 6-12-month postoperative CTs. Primary outcome was all-cause mortality, and secondary outcome was disease-free survival (DFS). Cox regression assessed the association between (1) BC at baseline and mortality and (2) the decrease in BC between baseline and follow-up scans and mortality/DFS. RESULTS: Model performance was high with Dice coefficients ≥0.94 ± 0.06. Among 299 patients with GEAC (age 63.0 ± 10.7 years; 19.4% female), 140 deaths (47%) occurred over a median follow-up of 31.3 months. At baseline, no BC measure was associated with DFS. Only a substantial decrease in VAT >70% after a 6- to 12-month follow-up was associated with mortality (hazard ratio [HR], 1.99 [95% CI, 1.18 to 3.34]; P = .009) and DFS (HR, 1.73 [95% CI, 1.01 to 2.95]; P = .045) independent of age, sex, BMI, Union for International Cancer Control stage, histologic grading, resection status, neoadjuvant therapy, and time between surgery and follow-up CT. CONCLUSION: DL enables opportunistic estimation of BC from routine staging CT to quantify prognostic information. In patients with GEAC, only a substantial decrease of VAT 6-12 months postsurgery was an independent predictor for DFS beyond traditional risk factors, which may help to identify individuals at high risk who go otherwise unnoticed.

Detection of Adenocarcinoma of the Colon on 18 F-Fluciclovine PET/CT.

ABSTRACT: An 85-year-old man with prostate cancer and de novo bone metastases was treated with hormonal therapy with resolution of bone lesions, improved primary disease, and improved serum tumor markers. Although on hormonal therapy, biochemical recurrence prompted performance of 18 F-fluciclovine PET/CT. Fluciclovine PET/CT revealed primary prostate cancer progression with incidental note of avid foci in the colon for which colonoscopy was recommended. Colonoscopy with biopsy was performed with pathology revealing primary colon adenocarcinoma. Before reinitiation of prostate cancer therapy, segmental colon resection was performed with pathology positive for additional sites of colon cancer.

Metastatic Lung Adenocarcinoma Received Combined 177 Lu-FAP-2286 Radiation Therapy and Targeted Therapy.

ABSTRACT: A 56-year-old man with metastatic lung adenocarcinoma received combined 177 Lu-FAP-2286 radiation therapy and targeted therapy. After 1 treatment cycle, improvement of symptoms and radiological remission was observed. Moreover, the patient did not report any adverse effects.

VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images.

BACKGROUND AND OBJECTIVE: Gland segmentation of pathological images is an essential but challenging step for adenocarcinoma diagnosis. Although deep learning methods have recently made tremendous progress in gland segmentation, they have not given satisfactory boundary and region segmentation results of adjacent glands. These glands usually have a large difference in glandular appearance, and the statistical distribution between the training and test sets in deep learning is inconsistent. These problems make networks not generalize well in the test dataset, bringing difficulties to gland segmentation and early cancer diagnosis. METHODS: To address these problems, we propose a Variational Energy Network named VENet with a traditional variational energy Lv loss for gland segmentation of pathological images and early gastric cancer detection in whole slide images (WSIs). It effectively integrates the variational mathematical model and the data-adaptability of deep learning methods to balance boundary and region segmentation. Furthermore, it can effectively segment and classify glands in large-size WSIs with reliable nucleus width and nucleus-to-cytoplasm ratio features. RESULTS: The VENet was evaluated on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset, the Colorectal Adenocarcinoma Glands (CRAG) dataset, and the self-collected Nanfang Hospital dataset. Compared with state-of-the-art methods, our method achieved excellent performance for GlaS Test A (object dice 0.9562, object F1 0.9271, object Hausdorff distance 73.13), GlaS Test B (object dice 94.95, object F1 95.60, object Hausdorff distance 59.63), and CRAG (object dice 95.08, object F1 92.94, object Hausdorff distance 28.01). For the Nanfang Hospital dataset, our method achieved a kappa of 0.78, an accuracy of 0.9, a sensitivity of 0.98, and a specificity of 0.80 on the classification task of test 69 WSIs. CONCLUSIONS: The experimental results show that the proposed model accurately predicts boundaries and outperforms state-of-the-art methods. It can be applied to the early diagnosis of gastric cancer by detecting regions of high-grade gastric intraepithelial neoplasia in WSI, which can assist pathologists in analyzing large WSI and making accurate diagnostic decisions.

Correlation of CT parameters and PD-L1 expression status in gastric cancer.

OBJECTIVE: We aimed to explore the correlation between routine computed tomography (CT) imaging features and programmed cell death ligand-1(PD-L1) expression status in gastric cancer and evaluate the predictive value of imaging parameters for this immunotherapy biomarker. MATERIALS AND METHODS: Patients with gastric adenocarcinoma who underwent abdominal CT three-stage enhanced scan and PD-L1 immunohistochemical testing before treatment were retrospectively examined. All diagnoses were confirmed through pathology. According to the expression status of PD-L1, they were divided into the positive (CPS ≥ 5) or negative group (CPS < 5). Baseline CT imaging features were collected. Diagnostic performances of the different variables were evaluated using receiver operating characteristic (ROC) curve. RESULTS: In total, 67 patients (17 women and 50 men; mean age: 59.55 ± 10.22 years) with gastric adenocarcinoma were included in the study. The overall stages, probability of maximum lymph node short diameter > 1 cm and peak of lesion enhancement occurring in the arterial phase were statistically significant between the two groups (p < 0.05). Moreover, the arterial enhancement fraction (AEF) was significantly higher in the positive group than that in the negative group (p < 0.05), and ROC curve analysis showed that the AEF exhibited a high evaluation efficacy (area under the curve [AUC] = 0.724 [95% confidence interval (CI): 0.602-0.826]). The combined parameters had the best diagnostic efficacy (AUC = 0.825 [95%CI: 0.716-0.933]), sensitivity (75.00%), and specificity (81.40%). CONCLUSIONS: These findings confirm a correlation between CT imaging features and PD-L1 expression status in gastric cancer, and AEF may help evaluate high PD-L1 expression and select patients suitable for immunotherapy.

Preoperative ultrasound-guided dual localization with titanium clips and carbon nanoparticles for predicting the surgical approach and guiding the resection of Siewert type II esophagogastric junction adenocarcinoma.

OBJECTIVE: To investigate the superiority of preoperative ultrasound-guided titanium clip and nanocarbon dual localization over traditional methods for determining the surgical approach and guiding resection of Siewert type II adenocarcinoma of the esophagogastric junction (AEG). METHOD: This study included 66 patients with Siewert type II AEG who were treated at the PLA Joint Logistics Support Force 900th Hospital between September 1, 2021, and September 1, 2023. They were randomly divided into an experimental group (n = 33), in which resection was guided by the dual localization technique, and the routine group (n = 33), in which the localization technique was not used. Surgical approach predictions, proximal esophageal resection lengths, pathological features, and the occurrence of complications were compared between the groups. RESULT: The use of the dual localization technique resulted in higher accuracy in predicting the surgical approach (96.8% vs. 75.9%, P = 0.02) and shorter proximal esophageal resection lengths (2.39 ± 0.28 cm vs. 2.86 ± 0.39 cm, P < 0.001) in the experimental group as compared to the routine group, while there was no significant difference in the incidence of postoperative complications (22.59% vs. 24.14%, P = 0.88). CONCLUSION: Preoperative dual localization with titanium clips and carbon nanoparticles is significantly superior to traditional methods and can reliably delineate the actual infiltration boundaries of Siewert type II AEG, guide the surgical approach, and avoid excessive esophageal resection.

TDASD: Generating medically significant fine-grained lung adenocarcinoma nodule CT images based on stable diffusion models with limited sample size.

BACKGROUND AND OBJECTIVES: Spread through air spaces (STAS) is an emerging lung cancer infiltration pattern. Predicting its spread through CT scans is crucial. However, limited STAS data makes this prediction task highly challenging. Stable diffusion is capable of generating more diverse and higher-quality images compared to traditional GAN models, surpassing the dominating GAN family models in image synthesis over the past few years. To alleviate the issue of limited STAS data, we propose a method TDASD based on stable diffusion, which is able to generate high-resolution CT images of pulmonary nodules corresponding to specific nodular signs according to the medical professionals. METHODS: First, we apply the stable diffusion method for fine-tuning training on publicly available lung datasets. Subsequently, we extract nodules from our hospital's lung adenocarcinoma data and apply slight rotations to the original nodule CT slices within a reasonable range before undergoing another round of fine-tuning through stable diffusion. Finally, employing DDIM and Ksample sampling methods, we generate lung adenocarcinoma nodule CT images with signs based on prompts provided by doctors. The method we propose not only safeguards patient privacy but also enhances the diversity of medical images under limited data conditions. Furthermore, our approach to generating medical images incorporates medical knowledge, resulting in images that exhibit pertinent medical features, thus holding significant value in tumor discrimination diagnostics. RESULTS: Our TDASD method has the capability to generate medically meaningful images by optimizing input prompts based on medical descriptions provided by experts. The images generated by our method can improve the model's classification accuracy. Furthermore, Utilizing solely the data generated by our method for model training, the test results on the original real dataset reveal an accuracy rate that closely aligns with the testing accuracy achieved through training on real data. CONCLUSIONS: The method we propose not only safeguards patient privacy but also enhances the diversity of medical images under limited data conditions. Furthermore, our approach to generating medical images incorporates medical knowledge, resulting in images that exhibit pertinent medical features, thus holding significant value in tumor discrimination diagnostics.

Sinonasal adenocarcinoma presented as a giant anterior cranial fossa mass: a case report and review of the literature.

BACKGROUND: Intestinal adenocarcinoma accounts for less than 0.1-4% of all malignancies in the region. It is common among woodworkers and leather workers. Sinonasal adenocarcinoma usually arises from the ethmoid sinus (40%) or nasal cavity (25%). Extension to nearby structures is common, but intracranial spread is very rare. These tumors are usually treated with surgery, with a reported 5-year survival rate of 59% to 80%. CASE PRESENTATION: This is a 60-year-old Black African male patient who presented with globalized headache, nasal obstruction with snoring during sleep, anosmia, change in mentation, sometimes agitation and left-side visual loss of one-year duration with worsening his above symptoms over the last one month. He couldn't smell soap bilaterally; in his left eye he could see only hand movement at nearly 30 cm. On brain magnetic resonance imaging, there was a T1 hypo- and T2 hyper-intense anterior cranial fossa mass arising from the left ethmoid sinuses and sphenoid sinuses and compressing the left optic structures, and brain computed tomography demonstrated heterogeneous hypo- to isodense mass. Complete tumor excision achieved and discharged with significant improvement and linked to oncology unit for radiotherapy. CONCLUSION: The management of these patients is multidisciplinary, involving neurosurgeons, otolaryngologists, oncologists, and maxillofacial surgeons. Surgical resection is the main treatment strategy, followed by radiotherapy, particularly intensity-modulated therapy. Chemotherapy is used in highly advanced, metastatic, and unresectable tumors.

ENDOSONOGRAPHY-GUIDED RESCUE PROCEDURES AFTER FAILED ERCP IN A PATIENT WITH PANCREATIC DUCTAL ADENOCARCINOMA.

BACKGROUND: • The ERCP even when done by experienced professionals, fails in 10% of cases. BACKGROUND: • Until the development of the EUS-BD, PTBD had a role as a rescue therapy, despite a high rate of adverse events. BACKGROUND: • The EUS-BD is safe and has similar efficacy, when compared to PTBD and should be performed immediately after ERCP failure. BACKGROUND: • A doctor with skills in both methods (ERCP/EUS) is needed to determine the best EUS-guided therapeutic option.