Malignant Masses Research Articles

Accuracy of O-RADS System in Differentiating Between Benign and Malignant Adnexal Masses Assessed via External Validation by Inexperienced Gynecologists

Objective: To evaluate the accuracy of the O-RADS system in differentiating between benign and malignant adnexal masses, as assessed by inexperienced gynecologists. Methods: Ten gynecologic residents attended a 20 h training course on the O-RADS system conducted by experienced examiners. Following the training, the residents performed ultrasound examinations on patients admitted with adnexal masses under supervision, recording the data in a database that included videos and still images. The senior author later accessed this ultrasound database and presented the cases offline to ten residents for O-RADS rating, with the raters being blinded to the final diagnosis. The efficacy of the O-RADS system by the residents and inter-observer variability were assessed. Results: A total of 201 adnexal masses meeting the inclusion criteria were evaluated, consisting of 136 (67.7%) benign masses and 65 (32.3%) malignant masses. The diagnostic performance of the O-RADS system showed a sensitivity of 90.8% (95% CI: 82.2–96.2%) and a specificity of 86.8% (95% CI: 80.4–91.8%). Inter-observer variability in scoring was analyzed using multi-rater Fleiss Kappa analysis, yielding Kappa indices of 0.642 (95% CI: 0.641–0.643). The false positive rate was primarily due to the misclassification of solid components in classic benign masses as O-RADS-4 or O-RADS-5. Conclusions: The O-RADS system demonstrates high diagnostic performance in distinguishing benign from malignant adnexal masses, even when used by inexperienced examiners. However, the false positive rate remains relatively high, mainly due to the over-interpretation of solid-appearing components in classic benign lesions. Despite this, inter-observer variability among non-expert raters was substantial. Incorporating O-RADS system training into residency programs is beneficial for inexperienced practitioners. This study could be an educational model for gynecologic residency training for other systems of sonographic features.

Ultrasound S-detect system can improve diagnostic performance of less experienced radiologists in differentiating breast masses. A retrospective dual-center study.

To compare the performance of radiologists when assisted by an S-detect system with that of radiologists or an S-detect system alone in diagnosing breast masses on US images in a dual-center setting. US images were retrospectively identified 296 breast masses (150 benign, 146 malignant) by investigators at two medical centers. Six radiologists from the two centers independently analyzed the US images and classified each mass into categories 2 to 5. The radiologists then re-reviewed the images with the assistance of the S-detect system. The diagnostic value of radiologists alone, S-detect system alone, and S-detect alone, and radiologists + S-detect were analyzed and compared. Radiologists had significantly decreased the average false negative rate (FNR) for diagnosing breast masses using S-detect system (- 10.7%) (P < 0.001) and increased the area under the receiver operating characteristic curve (AUC) from 0.743 to 0.788 (P < 0.001). Seventy-seven out of 888 US images from six radiologists in this study were changed positively (from false negative to true positive or from false positive to true negative) with the S-detect, while 39 out of 888 US images were altered negatively. Radiologists had better performance for the diagnosis of malignant breast masses on US images with an S-detect system than without. The study used a dual-center design and compared the performance of the radiologists alone, the S-detect alone, and the radiologists combined with the S-detect. The study reported an improvement in sensitivity and AUC particularly for low to intermediate-level radiologists, involved cases and radiologists from two different centers, and compared the diagnostic value of using S-detect system for masses of different sizes.

Clinical Utility and Diagnostic Accuracy of ROMA, RMI, ADNEX, HE4, and CA125 in the Prediction of Malignancy in Adnexal Masses

Objective: We aimed to compare the clinical utility and diagnostic accuracy of the ADNEX model, ROMA score, RMI I, and RMI IV, as well as two serum markers (CA125 and HE4) in preoperative discrimination between benign and malignant adnexal masses (AMs). Methods: We conducted a retrospective study extracting all consecutive patients with AMs seen at our Institution between January 2015 and December 2020. Accuracy metrics included sensitivity (SE), specificity (SP), and area under the receiver operating characteristic curve (AUC), and their 95% confidence intervals (CI) were calculated for basic discrimination between AMs. Model performance was evaluated in terms of discrimination ability and clinical utility (net benefit, NB). Results: A total of 581 women were included; 481 (82.8%) had a benign ovarian tumor and 100 (17.2%) had a malignant tumor. The SE and SP of CA125, HE4, ROMA score, RMI I, RMI IV, and ADNEX model were 0.60 (0.54–0.66) and 0.80 (0.76–0.83); 0.39 (0.30–0.49) and 0.96 (0.94–0.98); 0.59 (0.50–0.68) and 0.92 (0.88–0.95); 0.56 (0.46–0.65) and 0.98 (0.96–0.99); 0.54 (0.44–0.63) and 0.96 (0.94–0.98); 0.82 (0.73–0.88) and 0.91 (0.89–0.94), respectively. The overall AUC was 0.76 (0.74–0.79) for CA125, 0.81 (0.78–0.83) for HE4, 0.82 (0.80–0.85) for ROMA, 0.86 (0.84–0.88) for RMI I, 0.83 (0.81–0.86) for RMI IV, and 0.92 (0.90–0.94) for ADNEX. The NB for ADNEX was higher than other biomarkers and models across all decision thresholds between 5% and 50%. Conclusions: The ADNEX model showed a better discrimination ability and clinical utility when differentiating malignant from benign Ams, compared to CA125, HE4, ROMA score, RMI I, and RMI IV.

Detection of a Gossypiboma as a Retrocardiac Mass in a Female Patient with a History of Coronary Artery Bypass Graft

Introduction: "Gossypiboma," also known as "textiloma," refers to retained surgical items left in the body after an operation. These retained items can lead to a range of issues, from asymptomatic cases to life-threatening complications. Diagnostically challenging, gossypibomas can mimic malignant masses, making it essential to consider them in the differential diagnosis of postoperative patients with unexplained symptoms. Imaging is crucial for detection and management. Case Presentation: A 69-year-old female with a history of coronary artery bypass graft (CABG) surgery presented with exertional shortness of breath. Imaging revealed a large mass near the left atrium. Initially suspected to be a tumor, further investigation identified it as retained surgical gauze from her surgery ten years prior. Despite the mass, the patient exhibited only mild symptoms, and given her stable condition, regular follow-ups were recommended. Conclusions: Gossypibomas are rare but serious complications that may surface years after surgery. This case highlights the importance of meticulous surgical practices and careful postoperative monitoring. It underscores the need for accurate gauze counting and effective communication in the operating room to prevent errors that can lead to health risks and potential legal issues.

Undifferentiated embryonal sarcoma of the liver masquerading as a cystadenoma in a young adult: a case report

BackgroundUndifferentiated embryonal sarcoma of the liver is an infrequent hepatic malignancy, primarily observed in the pediatric population. This neoplasm is exceedingly rare among adults. Despite its scarcity in adult cases, it remains imperative to accurately discern undifferentiated embryonal sarcoma of the liver utilizing diverse imaging modalities to prevent misdiagnosis with more prevalent benign and malignant hepatic masses. In this case, a comprehensive imaging examination was conducted, encompassing ultrasound, computed tomography, magnetic resonance imaging, and positron emission tomography–computed tomography scans. Notably, the positron emission tomography–computed tomography scan revealed 18F-fluorodeoxyglucose uptake characteristics indicative of malignancy, providing a pivotal clue for prompt diagnosis of undifferentiated embryonal sarcoma of the liver.Case presentationThis report presents the diagnostic procedure employed for a 37-year-old female patient of Han ethnicity in China diagnosed with undifferentiated embryonal sarcoma of the liver. The patient was admitted with a 2-day history of mid-upper quadrant abdominal pain. The patient’s temperature and inflammatory markers, such as white blood cell count and hypersensitive reactive protein, were slightly elevated. Ultrasound showed a cystic–solid nodule in the liver. The computed tomography revealed a cystic mass in the right lobe of the liver, characterized by a low-density shadow and a lack of significant enhancement during contrast-enhanced scanning, initially suggesting cystadenoma. Enhanced magnetic resonance imaging revealed a block-like abnormal signal shadow in the right hepatic lobe, suggestive of various etiologies, including benign lesions, cystadenoma with hemorrhage, or hemangioma with hemorrhage. However, the positron emission tomography–computed tomography showed increased 18F-fluorodeoxyglucose consumption within both the cystic wall and lesion, raising suspicion of malignancy. Surgical resection of the posterior hepatic lobe was performed under general anesthesia after comprehensive preoperative preparations. During the procedure, a space-occupying lesion was identified in the right posterior hepatic lobe, adhering to the diaphragm. Surface liver parenchyma overlying the tumor exhibited rupture, with several blood clots visible. The tumor was successfully and completely excised. Pathological examination revealed a fusiform cell tumor, necrosis, hemorrhage, cystic changes, cellular atypia, mitotic images, and eosinophilic globules suggestive of undifferentiated embryonal sarcoma of the liver. Immunohistochemical staining indicated CK (+), vimentin (Vim; +), Desmin (+), actin (−), α1-AT (+), GPC-3 (+), PDGFRa (+), MDM2 (+), P16 (+), Ki-67 (+) 30–35%, and PAS (+). The patient underwent five cycles of combination chemotherapy with ifosfamide and epirubicin, administered at 21-day intervals at our hospital. Following 3 years of postoperative surveillance, the patient remained stable, with no evidence of hepatic tumor recurrence.ConclusionOn the basis of this case and a comprehensive literature review, we recommend that clinicians consider the possibility of undifferentiated embryonal sarcoma of the liver in patients presenting with non-specific clinical and serological markers, particularly when there is inconsistency between ultrasound and computed tomography imaging findings, along with elevated 18F-fluorodeoxyglucose uptake observed in both the cystic wall and lesion on positron emission tomography–computed tomography examination. Given the rarity and high-grade malignancy of undifferentiated embryonal sarcoma of the liver, heightened clinical awareness and recognition are crucial for early diagnosis and successful therapy.

Differentiation of Benign and Malignant Masses in Mammogram Using 2D-Fourier–Bessel Intrinsic Band Functions and Improved Feature Space

Differentiation of Benign and Malignant Masses in Mammogram Using 2D-Fourier–Bessel Intrinsic Band Functions and Improved Feature Space

Diagnostic accuracy of ultrasound classifications – O-RADS US v2022, O-RADS US v2020, and IOTA SR – in distinguishing benign and malignant adnexal masses: Enhanced by combining O-RADS US v2022 with tumor marker HE4

PurposeTo assess the diagnostic accuracy of O-RADS Ultrasound (O-RADS US) v2022, O-RADS US v2020, and IOTA SR, and to evaluate whether combining imaging findings with tumor markers enhances the diagnosis of adnexal masses. MethodsThis retrospective study, conducted between January 2018 and December 2023, included consecutive women with adnexal masses scheduled for surgery. Histopathologic results served as the reference standard. Risk factors for malignancy were identified using univariate and multivariate logistic regression analyses. ROC analysis was employed to assess diagnostic test performances, while Kappa statistics evaluated inter-reviewer agreement. ResultsA total of 613 women (mean age, 49.39 ± 12.81 years; range, 16–87 years) with pelvic masses were included. O-RADS US v2022 exhibited comparable performance to O-RADS US v2020, with areas under the curve (AUC) values of 0.940 and 0.937, respectively (p = 0.02, exceeding the adjusted significance level of 0.0167). Both O-RADS models outperformed the IOTA SR, which had an AUC of 0.862 (p < 0.0001 for both comparisons). Multivariate analysis revealed that O-RADS US v2022 [OR 9.148, 95 %CI (4.912–17.039), p < 0.001] and HE4 [OR 1.023, 95 %CI (1.010–1.036), p = 0.001] were significant factors associated with malignant lesions. Furthermore, the combination of O-RADS US v2022 and HE4 demonstrated an AUC of 0.98, significantly outperforming either O-RADS US v2022 alone (AUC = 0.94) or HE4 alone (AUC = 0.92). The Kappa values for O-RADS US v2022, O-RADS US v2020 and IOTA SR were 0.933, 0.891 and 0.923, respectively, indicating substantial inter-reader agreement. ConclusionsThe O-RADS US v2022 demonstrates comparable performance in predicting ovarian malignant lesions when compared to O-RADS US v2020, while surpassing the performance of IOTA SR. Additionally, the combination of O-RADS US v2022 and HE4 provides improved diagnostic effectiveness over using either O-RADS US v2022 or HE4 alone.

The Value of Second-look Ultrasound and Mammography for Assessment and Biopsy of MRI-detected Breast Lesions

Rationale and ObjectivesSuspicious lesions detected in multiparametric breast MRI can be further analyzed with second-look ultrasound (SLUS) and/or mammography. This study aims to assess the value of second-look imaging in selecting the appropriate biopsy method for different lesion characteristics. Materials and MethodsBetween January 2021 and December 2023, 212 women underwent contrast-enhanced multiparametric breast MRI at 3.0 Tesla. A total of 241 suspicious lesions (108 malignancies, 44.8%) were further assessed with SLUS and second-look mammography. Subsequent image-guided biopsy of each lesion was performed using the most suitable modality. Size-dependent lesion detection rates in SLUS and mammography were compared by means of the McNemar test. ResultsLesions referred to MRI-guided biopsy were predominantly ≤ 10 mm in size (52.8%). SLUS displayed higher detection rates than mammography for mass lesions (55.6% [95% confidence interval 46.4–64.4%] versus 16.7% [10.6–24.3%]; p<0.001) with a particularly high sensitivity for malignant mass lesions > 10 mm (88.5 % [69.9–97.6%]). In contrast, the detection rate for malignant non-mass lesions was lower in SLUS than in second-look mammography (22.0% [11.5–36.0%] versus 38.0% [24.7–52.8%]; p<0.001). The malignancy rates in ultrasound-, mammography-, and MRI-guided biopsies were 53.7%, 55.2%, and 35.0%, respectively. ConclusionSLUS is an excellent tool for further assessment and biopsy of suspicious mass lesions > 10 mm without associated calcifications. In contrast, supplemental ultrasound is of limited value in the evaluation and biopsy guidance of suspicious non-mass lesions compared to second-look mammography.

Imaging phenotype evaluation from digital breast tomosynthesis data: A preliminary study

Background:Digital breast tomosynthesis (DBT) has been widely adopted as a supplemental imaging modality for diagnostic evaluation of breast cancer and confirmation studies. In this study, a deep learning-based method for characterizing breast tissue patterns in DBT data is presented. Methods:A set of 5388 2D image patches was produced from 230 right mediolateral oblique, 259 left mediolateral oblique, 18 right craniocaudal, and 15 left craniocaudal single-breast DBT studies, using slice-wise annotations of abnormalities and normal tissue. We implemented a patch classifier to predict samples according to two differing scenarios and train it using the patch dataset. First, tissue samples were classified into the following classes: malignant, benign, and normal breast tissue. Second, tissue samples were classified into the following classes: malignant mass, benign mass, benign architectural distortion, malignant architectural distortion, and normal breast tissue. We employed transfer learning and initialized the model base layers with existing pre-trained weights obtained from Globally-Aware Multiple Instance Classifier. Results:High class-wise recall values of 0.8906, 0.8541 and 0.7345 and specificities 0.9558, 0.9575 and 0.8830 were obtained for normal, benign, and malignant classification, respectively. More intricate classification yielded class-wise recall values of 0.8708, 0.8299, 0.9444 and 0.5723 and specificities 0.9406, 0.9833, 0.8943 and 0.9652 for benign mass, normal, malignant architectural distortion, and malignant mass, respectively. However, benign architectural distortion was confused with benign mass and malignant architectural distortion. Conclusions:Combining the proposed phenotype classifier with the commonly used malignant-benign-normal classification enables a more detailed assessment of digital breast tomosynthesis images.

An optimized siamese neural network with deep linear graph attention model for gynaecological abdominal pelvic masses classification.

An adnexal mass, also known as a pelvic mass, is a growth that develops in or near the uterus, ovaries, fallopian tubes, and supporting tissues. For women suspected of having ovarian cancer, timely and accurate detection of a malignant pelvic mass is crucial for effective triage, referral, and follow-up therapy. While various deep learning techniques have been proposed for identifying pelvic masses, current methods are often not accurate enough and can be computationally intensive. To address these issues, this manuscript introduces an optimized Siamese circle-inspired neural network with deep linear graph attention (SCINN-DLGN) model designed for pelvic mass classification. The SCINN-DLGN model is intended to classify pelvic masses into three categories: benign, malignant, and healthy. Initially, real-time MRI pelvic mass images undergo pre-processing using semantic-aware structure-preserving median morpho-filtering to enhance image quality. Following this, the region of interest (ROI) within the pelvic mass images is segmented using an EfficientNet-based U-Net framework, which reduces noise and improves the accuracy of segmentation. The segmented images are then analysed using the SCINN-DLGN model, which extracts geometric features from the ROI. These features are classified into benign, malignant, or healthy categories using a deep clustering algorithm integrated into the linear graph attention model. The proposed system is implemented on a Python platform, and its performance is evaluated using real-time MRI pelvic mass datasets. The SCINN-DLGN model achieves an impressive 99.9% accuracy and 99.8% recall, demonstrating superior efficiency compared to existing methods and highlighting its potential for further advancement in the field.

Improved diagnostic confidence using Super Resolution CEUS imaging in testicular lesions.

Ultrasound is the most used interdisciplinary non-ionizing imaging technique in clinical pathologies of the testis. The testis may be affected by a plethora of different disorders such as vasculopathies, trauma, infections and manifestations of primary and secondary malignant masses. Conventional ultrasound represents the basic imaging modality of choice to assess scrotal disorders. Contrast-enhanced ultrasound (CEUS) can provide further information to distinguish between benign and malignant testicular mass lesions. The recent introduction of Super Resolution CEUS Micro-Vascular Imaging (MVI SR) and Time of Arrival (TOA SR) parametric mapping compliments the information provided by conventional CEUS, since these two new post-processing techniques improve the visualization of microvascular structures with slow blood flow and provide high-resolution images of the peak contrast enhancement and temporal perfusion patterns. This paper gives a comprehensive overview of differential diagnoses of the testicular disorder and their corresponding sono-morphologic correlates based on representative cases of the Interdisciplinary Ultrasound Center of the University Hospital Munich.

The incremental value of Histogram Analysis in the differentiation between hyperdense cysts and solid renal masses on unenhanced CT images.

To investigate the utility of voxel histogram analysis (HA) for differentiating hyperdense renal cysts from small solid masses on unenhanced computed tomography (CT) scans. A retrospective analysis of 99 hyperdense cystic lesions and 28 solid malignant lesions was conducted using a radiological database (from 2015 to 2021) and a pathological database (from 2010 to 2020). The study investigated the distribution of voxel attenuation values using percentiles to establish reliable criteria for differentiation after drawing a region of interest (ROI) in the centre of the lesions. The standard of reference was a histopathological diagnosis for malignant masses and contrast-enhanced CT or magnetic resonance imaging (MRI) for cysts. HA provided higher diagnostic accuracy than the conventional mean attenuation value of 70 Hounsfield Units (HU). For the 75th and 90th percentiles ± 1 standard deviation, accuracies of 77.2% (95% confidence interval 68.9-84.2%) for the 75th and 68.5% (59.7-76.4%) for the 90th were found, versus 37.0% (28.6-46.0%) for the 70 HU threshold criterion. A Gaussian distribution of voxel attenuation values was observed in 88.9% of the lesions, suggesting that it is feasible to calculate these parameters from a single measurement. The study underscores the potential of HA as a valuable tool for characterizing hyperdense cysts on unenhanced CT by using the same ROI for measuring lesion attenuation. HA could offer additional value beyond the 70 HU criterion and possibly influence clinical decisions. Multi-institutional studies are necessary for external validation to confirm its generalizability and more extensive applicability.

Percutaneous Aspiration Injection and Re-aspiration as A Minimally Invasive Treatment for Spinal Cystic Echinococcosis: A Case Report.

Cystic echinococcosis is a parasitic disease with significant importance for public health in endemic regions. Spinal cystic echinococcosis, however, is a rare form that may lead to severe complications due to its localization. In this manuscript, we presented a 16-year-old male patient who admitted with abdominal and back edema for 2 months, evaluated with preliminary diagnoses of Pott's abscess and malignant mass, subsequently diagnosed with spinal cystic echinococcosis. It was concluded that cystic echinococcosis should be considered in differential diagnosis of large cystic masses and percutaneous aspiration, injection, reaspiration method might be a safe and effective treatment option particularly for cases of complicated spinal cystic echinococcosis.

8248 Glucocorticoid Induced Adrenal Crisis Masked By Metastatic Lung Carcinoma

Abstract Disclosure: N. Abrahimi: None. A. Abrahimi: None. Background: Chronic glucocorticoid use of three weeks or longer can cause hypothalamic-pituitary-adrenal axis inhibition by negative feedback, causing adrenal suppression. (1) Adrenal crisis is diagnosed by an acute clinical decompensation, persistent hypotension that resolves with high dose steroid administration. (2) Adrenal crisis is precipitated by several causes such as illness, dehydration, trauma, emotional distress and sometimes due to no identifiable cause. (2) Clinical Case: 64-year-old male past medical history of lung carcinoma metastatic to mediastinum who presented to the hospital with tachycardia. On presentation, patient was tachycardic, tachypneic, and hypotensive. Labs significant for WBC 22, Na+ 129, K+ 3.2, Cr 3.4. Patient received 2.5 L bolus of NS and was given one dose of azithromycin due to concern for sepsis. After fluid resuscitation, hypotension and tachycardia resolved. Presenting symptoms included ongoing nausea, decreased appetite and malaise for several weeks. Repeat labs post fluid resuscitation showed resolution of leukocytosis, improvement of AKI, and repletion of electrolytes. CT chest abdomen pelvis showed questionable pneumonia, however, consolidations were difficult to distinguish from known malignant masses. Clinical picture was not consistent with pneumonia and so additional antibiotics were held. Several hours later, blood pressure decreased, and patient began requiring vasopressors. Cefepime was started given concern for septic shock. Nearly 48 hours after initiation of broad-spectrum antibiotics hypotension persisted and patient remained on vasopressors. Given clinical decline, etiology of shock was reconsidered. Patient reported daily prednisone use over the last several months for COPD management. Given high clinical suspicion of adrenal crisis in setting of chronic steroid use and glucocorticoid withdrawal, patient was started on hydrocortisone 100 mg every 8 hours. Shortly after initiation of stress dosed steroids, the patient was successfully weaned off vasopressors with complete resolution of hypotension. Conclusion: Presentation and symptoms of adrenal insufficiency and crisis including nausea, anorexia, fatigue, malaise, hypotension and electrolyte abnormalities are nonspecific, can go unrecognized and be masked by other disease processes, such as metastatic cancer. (1) Adrenal crisis is life threatening and must be diagnosed and treated early to prevent mortality. (2) Therefore, adrenal crisis should always be on the differential in patients who present with shock.

Multiarm, non-randomised, single-centre feasibility study—investigation of the differential biology between benign and malignant renal masses using advanced magnetic resonance imaging techniques (IBM-Renal): protocol

IntroductionLocalised renal masses are an increasing burden on healthcare due to the rising number of cases. However, conventional imaging cannot reliably distinguish between benign and malignant renal masses, and renal...

Using the GoogLeNet deep-learning model to distinguish between benign and malignant breast masses based on conventional ultrasound: a systematic review and meta-analysis.

Breast cancer is one of the most common malignancies in women worldwide, and early and accurate diagnosis is crucial for improving treatment outcomes. Conventional ultrasound (CUS) is a widely used screening method for breast cancer; however, the subjective nature of interpreting the results can lead to diagnostic errors. The current study sought to estimate the effectiveness of using a GoogLeNet deep-learning convolutional neural network (CNN) model to identify benign and malignant breast masses based on CUS. A literature search was conducted of the Embase, PubMed, Web of Science, Wanfang, China National Knowledge Infrastructure (CNKI), and other databases to retrieve studies related to GoogLeNet deep-learning CUS-based models published before July 15, 2023. The diagnostic performance of the GoogLeNet models was evaluated using several metrics, including pooled sensitivity (PSEN), pooled specificity (PSPE), the positive likelihood ratio (PLR), the negative likelihood ratio (NLR), the diagnostic odds ratio (DOR), and the area under the curve (AUC). The quality of the included studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies Scale (QUADAS). The eligibility of the included literature were independently searched and assessed by two authors. All of the 12 studies that used pathological findings as the gold standard were included in the meta-analysis. The overall average estimation of sensitivity and specificity was 0.85 [95% confidence interval (CI): 0.80-0.89] and 0.86 (95% CI: 0.78-0.92), respectively. The PLR and NLR were 6.2 (95% CI: 3.9-9.9) and 0.17 (95% CI: 0.12-0.23), respectively. The DOR was 37.06 (95% CI: 20.78-66.10). The AUC was 0.92 (95% CI: 0.89-0.94). No obvious publication bias was detected. The GoogLeNet deep-learning model, which uses a CNN, achieved good diagnostic results in distinguishing between benign and malignant breast masses in CUS-based images.

Role of inter- and extra-lesion tissue, transfer learning, and fine-tuning in the robust classification of breast lesions

Accurate and unbiased classification of breast lesions is pivotal for early diagnosis and treatment, and a deep learning approach can effectively represent and utilize the digital content of images for more precise medical image analysis. Breast ultrasound imaging is useful for detecting and distinguishing benign masses from malignant masses. Based on the different ways in which benign and malignant tumors affect neighboring tissues, i.e., the pattern of growth and border irregularities, the penetration degree of the adjacent tissue, and tissue-level changes, we investigated the relationship between breast cancer imaging features and the roles of inter- and extra-lesional tissues and their impact on refining the performance of deep learning classification. The novelty of the proposed approach lies in considering the features extracted from the tissue inside the tumor (by performing an erosion operation) and from the lesion and surrounding tissue (by performing a dilation operation) for classification. This study uses these new features and three pre-trained deep neuronal networks to address the challenge of breast lesion classification in ultrasound images. To improve the classification accuracy and interpretability of the model, the proposed model leverages transfer learning to accelerate the training process. Three modern pre-trained CNN architectures (MobileNetV2, VGG16, and EfficientNetB7) are used for transfer learning and fine-tuning for optimization. There are concerns related to the neuronal networks producing erroneous outputs in the presence of noisy images, variations in input data, or adversarial attacks; thus, the proposed system uses the BUS-BRA database (two classes/benign and malignant) for training and testing and the unseen BUSI database (two classes/benign and malignant) for testing. Extensive experiments have recorded accuracy and AUC as performance parameters. The results indicate that the proposed system outperforms the existing breast cancer detection algorithms reported in the literature. AUC values of 1.00 are calculated for VGG16 and EfficientNet-B7 in the dilation cases. The proposed approach will facilitate this challenging and time-consuming classification task.

S2810 Colonic Deception: Two Cases of Ischemic Colitis Posing as Malignant Masses

S2810 Colonic Deception: Two Cases of Ischemic Colitis Posing as Malignant Masses

S3088 A Case of Atypical Endometriosis Presenting as a Malignant Appearing Colonic Mass

S3088 A Case of Atypical Endometriosis Presenting as a Malignant Appearing Colonic Mass

Trophoblastic disease and choriocarcinoma

Gestational trophoblastic disease (GTD) is a group of diseases associated with pregnancies that demonstrate abnormal development of trophoblastic cells. GTD includes hydatidiform moles (HM) that may continue to further develop into gestational trophoblastic neoplasms (GTN), such as choriocarcinoma (CC). Gestational CC is a malignant mass development that may arise from HM, from other (normal) pregnancies or from other gestational events (such as ectopic pregnancies). The aim of this review is to outline current understating of the genetics and epigenetics of GTD and gestational CC and the link between the two diseases.