Malignant Mass Lesions Research Articles

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.

Comprehensive Evaluation of Pulmonary Masses Using Multi-detector CT: Correlating Morphological Features With CT-Guided Histopathological Findings for Enhanced Diagnostic Accuracy.

Lung masses pose a significant diagnostic challenge due to their diverse causes, from benign hamartomas to malignant bronchogenic carcinoma. Multi-detector CT (MDCT) is essential in evaluating these masses, offering detailed morphological insights to help differentiate between benign and malignant lesions. However, a definitive diagnosis often requires histopathological confirmation. CT-guided biopsy is crucial, providing a minimally invasive method to obtain tissue samples and thus guiding clinical management and treatment decisions. The primary objective of this study was to assess the diagnostic accuracy of MDCT in differentiating between benign and malignant lung mass lesions. The study focused on evaluating the characteristic features of lung masses on MDCT that aid in this differentiation, correlating imaging findings with histopathological results from CT-guided biopsies, and determining the overall diagnostic utility of MDCT in thoracic lesions. This hospital-based observational study was conducted over a period of 17 months. The study included 52 patients with thoracic lesions, identified through imaging techniques such as chest radiographs and CT scans. These patients underwent CT-guided biopsy, with tissue samples sent for histopathological examination. Inclusion criteria involved patients with clinically and radiographically diagnosed lung masses, while exclusion criteria included those who did not consent, had contraindications to contrast media, or had conditions such as severe respiratory distress or coagulopathy. The data were analyzed using descriptive statistics, with efficacy measures such as yield and failure rates of CT-guided biopsies and validation measures like sensitivity, specificity, positive predictive value, and negative predictive value for MDCT. The study included 52 patients (50% male, 50% female), aged 16-80, with the most common age group being 51-60 years. Lesions were mainly in the lung parenchyma (65.38%), followed by the mediastinum (15.38%), hilar region (11.54%), and pleura (7.69%). MDCT evaluation classified 84.44% of the lesions as malignant, characterized by irregular contours, inhomogeneous texture, and contrast enhancement, while 15.55% were benign. Histopathology confirmed 42 malignant lesions, with squamous cell carcinoma being the most prevalent. Benign lesions included abscesses, tuberculosis, and pneumonitis. The study achieved a 100% success rate for CT-guided biopsy, with one minor complication (pneumothorax). The diagnostic accuracy of MDCT was notable, with a sensitivity of 100% for detecting malignancies, a specificity of 77.78%, a positive predictive value of 87.50%, and a negative predictive value of 100%, emphasizing its effectiveness in thoracic lesion evaluation. MDCT is a highly effective tool in the evaluation of lung masses, providing critical information that aids in distinguishing between benign and malignant lesions. When combined with CT-guided biopsy, it offers a reliable method for obtaining diagnostic tissue samples, with a high degree of accuracy and a low complication rate. The study underscores the importance of integrating imaging and histopathological findings in the management of thoracic lesions, ultimately enhancing diagnostic precision and informing appropriate clinical interventions.

Intelligent breast cancer diagnosis with two-stage using mammogram images

Breast cancer (BC) significantly contributes to cancer-related mortality in women, underscoring the criticality of early detection for optimal patient outcomes. Mammography is a key tool for identifying and diagnosing breast abnormalities; however, accurately distinguishing malignant mass lesions remains challenging. To address this issue, we propose a novel deep learning approach for BC screening utilizing mammography images. Our proposed model comprises three distinct stages: data collection from established benchmark sources, image segmentation employing an Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet (ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN models are optimized using the Modified Mussel Length-based Eurasian Oystercatcher Optimization (MML-EOO) algorithm. The performance is evaluated using a variety of metrics, and a comparative analysis against conventional methods is presented. Our experimental results reveal that the proposed BC detection framework attains superior precision rates in early disease detection, demonstrating its potential to enhance mammography-based screening methodologies.

Diagnostic efficiency of multi-modal MRI based deep learning with Sobel operator in differentiating benign and malignant breast mass lesions-a retrospective study.

To compare the diagnostic efficiencies of deep learning single-modal and multi-modal for the classification of benign and malignant breast mass lesions. We retrospectively collected data from 203 patients (207 lesions, 101 benign and 106 malignant) with breast tumors who underwent breast magnetic resonance imaging (MRI) before surgery or biopsy between January 2014 and October 2020. Mass segmentation was performed based on the three dimensions-region of interest (3D-ROI) minimum bounding cube at the edge of the lesion. We established single-modal models based on a convolutional neural network (CNN) including T2WI and non-fs T1WI, the dynamic contrast-enhanced (DCE-MRI) first phase was pre-contrast T1WI (d1), and Phases 2, 4, and 6 were post-contrast T1WI (d2, d4, d6); and Multi-modal fusion models with a Sobel operator (four_mods:T2WI, non-fs-T1WI, d1, d2). Training set (n=145), validation set (n=22), and test set (n=40). Five-fold cross validation was performed. Accuracy, sensitivity, specificity, negative predictive value, positive predictive value, and area under the ROC curve (AUC) were used as evaluation indicators. Delong's test compared the diagnostic performance of the multi-modal and single-modal models. All models showed good performance, and the AUC values were all greater than 0.750. Among the single-modal models, T2WI, non-fs-T1WI, d1, and d2 had specificities of 77.1%, 77.2%, 80.2%, and 78.2%, respectively. d2 had the highest accuracy of 78.5% and showed the best diagnostic performance with an AUC of 0.827. The multi-modal model with the Sobel operator performed better than single-modal models, with an AUC of 0.887, sensitivity of 79.8%, specificity of 86.1%, and positive prediction value of 85.6%. Delong's test showed that the diagnostic performance of the multi-modal fusion models was higher than that of the six single-modal models (T2WI, non-fs-T1WI, d1, d2, d4, d6); the difference was statistically significant (p = 0.043, 0.017, 0.006, 0.017, 0.020, 0.004, all were greater than 0.05). Multi-modal fusion deep learning models with a Sobel operator had excellent diagnostic value in the classification of breast masses, and further increase the efficiency of diagnosis.

Extracellular RNA in melanoma: Advances, challenges, and opportunities.

Melanoma, a malignant mass lesion that originates in melanocytes and has a high rate of malignancy, metastasis, and mortality, is defined by these characteristics. Malignant melanoma is a kind of highly malignant tumor that produces melanin and has a high mortality rate. Its incidence accounts for 1%-3% of all malignant tumors and shows an obvious upward trend. The discovery of biomolecules for the diagnosis and treatment of malignant melanoma has important application value. So far, the exact molecular mechanism of melanoma development relevant signal pathway still remains unclear. According to previous studies, extracellular RNAs (exRNAs) have been implicated in tumorigenesis and spread of melanoma. They can influence the proliferation, invasion and metastasis of melanoma by controlling the expression of target genes and can also influence tumor progression by participating in signal transduction mechanisms. Therefore, understanding the relationship between exRNA and malignant melanoma and targeting therapy is of positive significance for its prevention and treatment. In this review, we did an analysis of extracellular vesicles of melanoma which focused on the role of exRNAs (lncRNAs, miRNAs, and mRNAs) and identifies several potential therapeutic targets. In addition, we discuss the typical signaling pathways involved in exRNAs, advances in exRNA detection and how they affect the tumor immune microenvironment in melanoma.

A Deep Learning–Based System Trained for Gastrointestinal Stromal Tumor Screening Can Identify Multiple Types of Soft Tissue Tumors

The accuracy and timeliness of the pathologic diagnosis of soft tissue tumors (STTs) critically affect treatment decision and patient prognosis. Thus, it is crucial to make a preliminary judgement on whether the tumor is benign or malignant with hematoxylin-and-eosin-stained images. Here, we present a deep learning-based system, STT-BOX, with only hematoxylin-and-eosin images for malignant STT identification from benign STTs with histopathologic similarity. STT-BOX assumed gastrointestinal stromal tumor as a baseline for malignant STT evaluation, and distinguished gastrointestinal stromal tumor from leiomyoma and schwannoma with 100% area under the curve in patients from three hospitals, which achieved higher accuracy than the interpretation of experienced pathologists. Particularly, this system performed well on six common types of malignant STTs from The Cancer Genome Atlas data set, accurately highlighting the malignant mass lesion. Moreover, without any fine-tuning, STT-BOX was capable of distinguishing ovarian malignant sex-cord stromal tumors. Our study includes mesenchymal tumors that originated from the digestive system, bone and soft tissues, and reproductive system, where the high accuracy of migration verification may reveal the morphologic similarity of the nine types of malignant tumors. Further evaluation in a pan-STT setting would be potential and prospective, obviating the overuse of immunohistochemistry and molecular tests, and providing a practical basis for clinical treatment selection in a timely manner.

Comparison of Ultrafast Dynamic Contrast-Enhanced (DCE) MRI with Conventional DCE MRI in the Morphological Assessment of Malignant Breast Lesions

Ultrafast (UF) dynamic contrast-enhanced (DCE)-MRI offers the potential for a faster and, therefore, less expensive examination of breast lesions; however, there are no reports that have evaluated whether UF DCE-MRI can be used the same as conventional DCE-MRI in the reading of morphological information. This study evaluated the agreement in morphological information obtained from malignant breast mass lesions between UF DCE-MRI and conventional DCE-MRI. UF DCE-MRI data were obtained over the first 60 s post-contrast injection, followed by the conventional DCE images. Two readers evaluated the size and morphology of the lesions in the final phase of the UF DCE-MRI and the early phase of the conventional DCE-MRI. Inter-method agreement in morphological information was evaluated for the two readers using the intraclass correlation coefficient for size, and the kappa statistics for the morphological descriptors. Differences in the proportion of each descriptor were examined using Fisher's test of independence. Most inter-method agreements were higher than substantial. UF DCE-MRI showed a circumscribed margin and homogeneous enhancement more often than conventional imaging. However, the percentages of readings showing the same morphology assessment between the UF DCE-MRI and conventional DCE-MRI were 71.2% (136/191) for Reader 1 and 69.1% (132/191) for Reader 2. We conclude that UF DCE-MRI may replace conventional DCE-MRI to evaluate the morphological information of malignant breast mass lesions.

ROLE OF DIFFUSION WEIGHTED IMAGING AND ADC VALUES IN CHARACTERISING BREAST LESIONS ON MR MAMMOGRAPHY

Background- Dynamic contrast enhanced MRI (DCE MR) is an important imaging modality to detect and evaluate the breast lesions with high sensitivity. Aim of study was to characterize breast lesions detect Objective- ed on MR mammography by diffusion weighted imaging and quantify ADC values to differentiate benign and malignant mass and non mass lesions. Methods- This prospective study was performed in thirty female patients on 1.5T GE Healthcare HDXT MRI machine. Thirty three breast lesions were included in this study. Final diagnosis was correlated with histopathological diagnosis. Out of total thirty three lesions, mean ADC value Results- of malignant lesions was 1.01 ± 0.24 x 10-3mm2/sec and for benign lesions was 1.48 ± 0.29 x 10-3 mm2/sec. The calculated ADC cut off value of 1.22x 10-3 mm2/sec gave a sensitivity of 86.96% and specicity of 90% to differentiate malignant and benign breast lesions. As a Conclusion- conclusion Diffusion weighted imaging and quantitative analysis with ADC values can be used with conventional DCE MR to differentiate benign and malignant breast lesions with high sensitivity and specicity

Sinonasal mass lesions: A clinicopathological study with p63 and p16 immunohistochemical expressions.

The worldwide annual incidence of carcinomas of the sinonasal tract is 0.5 to 1.0 patients per 100,000 per year. P63 plays a role in epithelial development and is used as a marker for basal and myoepithelial cells. Expression of p16 occurs as a result of functional inactivation of the retinoblastoma protein (pRb) by the human papilloma virus (HPV) E7 protein. This study aims to study the histological spectrum of benign and malignant sinonasal mass lesions and to study the immunohistochemical expression of p63 in different type of sinonasal mass lesions. It also aims to ascertain the incidence of high-risk HPV in primary sinonasal mass lesions with p16 immunohistochemistry and delineate the histological spectrum of HPV-related sinonasal lesions. This cross-sectional study was conducted on 80 cases from June 2018 to June 2020 at a tertiary care hospital. Clinical history including demographic parameters were collected in the study proforma. The gross findings of the specimens noted and histopathological examination by H&E staining done. Immunohistochemistry staining for p63 and p16 expression was performed on all cases. Most common age group affected was 41-60 years with male:female ratio of 1.67:1. Nonneoplastic lesions (38.7%) comprised majority of the cases followed by benign neoplastic lesions (31.3%) and malignant neoplastic lesions (30%). Among the malignant neoplastic lesions, p63 showed positive expression in 75% (p = 0.005) and p16 showed positive expression in 41.7% (p = 0.023). Among benign and nonneoplastic lesions, p63 showed positivity in 21.4% (p = 0.000) and p16 showed positivity in 44.6% (p = 0.040). We analyzed p63 and p16 expression in varied lineages like carcinomas, papillomas, and neuroectodermal differentiation arising from the sinonasal tract and also in relation to other clinicopathological parameters. This study revealed p63 expression was associated more with the squamous cell carcinomas and nasopharyngeal carcinomas. Sinonasal tract malignancies are also associated with HPV infections that are identifiable by p16 immunostaining and, thus, could provide new prospects in identifying any definite biological and clinical characteristics associated with HPV as well as advancement in the targeted therapies for this patient population.

Measurement of malignant spiculated mass lesions on mammogram: Do we include the length of the spicules?

The aim of this study is to determine if the core size or size with spicules has a better correlation with the final histologic size of spiculated mass lesions. A retrospective study of 48-month duration from January 2014 to December 2017 of biopsy-proven invasive ductal carcinoma presenting as spiculated mass lesions on mammogram was conducted. There were 195 patients in the study. The mean of the core size was 16.6 mm; when spicules were included the mean size was 27.4mm and final histologic size 21.1 mm. Using unpaired Student 't' test difference in the means was statistically significant (p<0.0001). Pearson number (R) core size versus final histologic size was 0.535 (P < 0.001) and for size with spicules versus final histologic size was 0.495 (P < 0.001). Our study demonstrated that the core size has a stronger positive correlation to final histologic size and should be used preoperatively in decision-making about surgery.

Persistent Mullerian Duct Syndrome with Malignant Conversion of Mullerian Remnant

A case report of a very rare entity of persistent Müllerian duct syndrome with malignant transformation of Mullerian remnant is presented. A twenty-two-year-old phenotypically normal male presented with complain of hematuria and lower abdominal pain. On examination patient had bilateral cryptorchidism and on imaging bilateral duplication of pelvicalyceal system and ureters and left sided ureterocoele. There was a malignant mass lesion with metastatic lymphadenopathy in pelvis. The diagnosis of persistent Mullerian duct syndrome was suspected on MRI pelvis and confirmed by genetic and pathological testings. Risk of malignant transformation of undescended testis and Mullerian remnants increases with age and have impact on psychological health of patient, so early diagnosis becomes crucial.

Combined Use of Texture Features and Morphological Classification Based on Dynamic Contrast-enhanced MR Imaging: Differentiating Benign and Malignant Breast Masses with High Negative Predictive Value.

Purpose: We evaluated the diagnostic performance of the texture features of dynamic contrast-enhanced (DCE) MRI for breast cancer diagnosis in which the discriminator was optimized, so that the specificity was maximized via the restriction of the negative predictive value (NPV) to greater than 98%.Methods: Histologically proven benign and malignant mass lesions of DCE MRI were enrolled retrospectively. Training and testing sets consist of 166 masses (49 benign, 117 malignant) and 50 masses (15 benign, 35 malignant), respectively. Lesions were classified via MRI review by a radiologist into 4 shape types: smooth (S-type, 34 masses in training set and 8 masses in testing set), irregular without rim-enhancement (I-type, 60 in training and 14 in testing), irregular with rim-enhancement (R-type, 56 in training and 22 in testing), and spicula (16 in training and 6 in testing). Spicula were immediately classified as malignant. For the remaining masses, 298 texture features were calculated using a parametric map of DCE MRI in 3D mass regions. Masses were classified into malignant or benign using two thresholds on a feature pair. On the training set, several feature pairs and their thresholds were selected and optimized for each mass shape type to maximize specificity with the restriction of NPV > 98%. NPV and specificity were computed using the testing set by comparison with histopathologic results and averaged on the selected feature pairs.Results: In the training set, 27, 12, and 15 texture feature pairs are selected for S-type, I-type, and R-type masses, respectively, and thresholds are determined. In the testing set, average NPV and specificity using the selected texture features were 99.0% and 45.2%, respectively, compared to the NPV (85.7%) and specificity (40.0%) in visually assessed MRI category-based diagnosis.Conclusion: We, therefore, suggest that the NPV of our texture-based features method described performs similarly to or greater than the NPV of the MRI category-based diagnosis.

Assessment of the diagnostic accuracy of contrast-enhanced digital mammography in the differentiation between benign and malignant breast mass lesions

Background: It is important to find an accurate and cost-effective tool for the early diagnosis of breast cancer which is the most common cancer in women worldwide. Contrast-Enhanced Digital Mammography(CEDM) is an emerging modality in depicting abnormalities seen at screening mammography and it could demonstrate disease extension and help in detecting response to therapy. This study aimed to evaluate the accuracy of CEDM in the diagnosis and differentiation between benign and malignant breast masses and mass-like lesions. Patients & methods: This prospective study included 100 female patients with 154 breast lumps, their ages ranged from 30-75 years (Mean= 42±5.3) who referred with a suspicious breast lump. Informed consent was obtained from all patients. All patients were subjected to local breast examination of both breast sides, renal function tests; Conventional mammography (CM), breast ultrasonography (U/S), and contrast-enhanced digital mammography (CEDM). Diagnoses confirmed by biopsy and histopathology results that used as a gold standard. Results: The 154 studied lesions found to be: 122 malignant neoplastic lesions (79.22%) and 32 benign lesions (20.78%) according to the histopathology results.CEDM was found to have higher sensitivity (97.56%), specificity (93.55%), and accuracy (96.75%) when compared to combined CM and U/S results that showed sensitivity (62.30%), specificity (68.75%), and accuracy (63.64%), Conclusion: Contrast-enhanced digital mammography found to be a low-cost effective diagnostic method in the differentiation between benign and malignant breast mass lesions that can improve diagnostic accuracy and increase the cancer detection rate.

Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification.

Since computer-aided diagnosis (CAD) schemes of medical images usually computes large number of image features, which creates a challenge of how to identify a small and optimal feature vector to build robust machine learning models, the objective of this study is to investigate feasibility of applying a random projection algorithm (RPA) to build an optimal feature vector from the initially CAD-generated large feature pool and improve performance of machine learning model. We assemble a retrospective dataset involving 1,487 cases of mammograms in which 644 cases have confirmed malignant mass lesions and 843 have benign lesions. A CAD scheme is first applied to segment mass regions and initially compute 181 features. Then, support vector machine (SVM) models embedded with several feature dimensionality reduction methods are built to predict likelihood of lesions being malignant. All SVM models are trained and tested using a leave-one-case-out cross-validation method. SVM generates a likelihood score of each segmented mass region depicting on one-view mammogram. By fusion of two scores of the same mass depicting on two-view mammograms, a case-based likelihood score is also evaluated. Comparing with the principle component analyses, nonnegative matrix factorization, and Chi-squared methods, SVM embedded with RPA yielded a significantly higher case-based lesion classification performance with the area under ROC curve of 0.84 ± 0.01 (p<0.02). The study demonstrates that RPA is a promising method to generate optimal feature vectors and improve SVM performance. This study presents a new method to develop CAD schemes with significantly higher and robust performance.

What makes it a Primary Ovarian Pseudomyxoma Peritonei When there are Mucinous Tumours of both Ovary and Appendix

Pseudomyxoma peritonei (PMP) is a rare condition characterized by gelatinous material within the abdominal cavity and on peritoneal surfaces arising from a mucinous neoplasm originating most commonly from appendix followed by ovary. A 48-year-old woman presented with abdominal bloating and weight loss for four months. Imaging studies showed malignant mass lesion arising from left ovary probably a mucinous adenocarcinoma with mucinous material in abdomen. She underwent cytoreduction and hyperthermic intraperitoneal chemotherapy. The left ovary was multiloculated filled with mucinous material and a focal solid area containing pultaceous material and tufts of hair. The appendix showed mucinous areas on the serosa. Microscopically, the left ovary showed multiple closely apposed cysts lined by a single layer of cuboidal to columnar mucinous epithelium with focal undulation and stratification. Also noted were few cysts walled by hair follicles and lobules of adipocytes. The appendix mucosa focally showed tubular glands with tufting lined by mucinous cells. Mucin pools were seen within the lamina propria, serosa and mesoappendicular fat. Pelvic peritoneum, falciform ligament, left upper and right lower quadrant peritoneum, rectal peritoneum, liver capsule and omentum showed acellular mucin. Low-grade appendiceal mucinous neoplasm (LAMN) and primary ovarian mucinous cystadenoma arising in mature cystic teratoma (MCT) with PMP were rendered. We report this case for its rarity. We would like to emphasize with this case the need to completely examine the appendix and thoroughly sample ovarian mucinous tumours to look for teratomatous component in all cases of PMP.

A Case of Fibrosing Mediastinitis Mimicking Malignant Mass Lesion with Tracheal Involvement, Diagnosed by Video-assisted Mediastinoscopy

A Case of Fibrosing Mediastinitis Mimicking Malignant Mass Lesion with Tracheal Involvement, Diagnosed by Video-assisted Mediastinoscopy

Anterior Mediastinal, Periaortik ve Plevral Multisentrik İdiyopatik Fibrozis Tanılı İki Olgu: Bilgisayarlı Tomografi ile Uzun Süreli Takip Sonuçları

Intrathoracic fibrosis is a rare disease with a benign histopathology mimicking malignant mass lesions which can be seen in infiltrative or limited mass form and can be followed by computed tomography as a primary imaging modality. In the literature, there are cases of mediastinal or pleural fibrosis. However, in our 2 cases, pleural involvement, retrosternal mass, and posterior mediastinal mass surrounding the aorta are seen at the same time. In particular, both parasternal pleural thickening in the form of bat wings is noteworthy. We could not find a published case in which 3 separate components were involved at the same time, followed up for a long time such as 8 and 6 years. Multicentric intrathoracic fibrosis cases were investigated with pre-diagnoses of thymoma, lymphoma, asbestos, and mesothelioma, diagnosed by biopsy and followed up in our hospital for 6-8 years, are presented because they are rare and instructive.

Cervical Cancer in a 25-Year Old Woman: Case Report.

A case of female 25 years old, married for 5 years, illiterate and she has 3 children from Basrah, Southern Iraq, she had history of recurrent vaginal bleeding during the second and 3rd trimesters in the last pregnancy unfortunately she was missed diagnosed during pregnancy as pregnancy cause of vaginal bleeding and was delivered by caesarian section due to cervical mass and bleeding with no history of follow up after delivery and continuous irregular vaginal bleeding 2 months after delivery till the last consultation when she developed severe pallor and severe vaginal bleeding . The patient was admitted to the Maternal and Child Hospital, Basrah, Iraq. On speculum examination, the cervix was fusiform, enlarged, barrel in shape, friable and bled on touch. Hemoglobin level was 6 gm/dl. Ultrasound and Magnetic Resonance Imaging (MRI) confirmed the presence of a large 7X5X6 Cm. cervical malignant solid mass lesion seen invading the upper third of vagina and lower endometrial cavity. No invasion to the rectum and urinary bladder. Total abdominal hysterectomy, bilateral salpingoophorectomy along with the removal of upper third of the vagina were done after blood correction. The patients agreed that future fertility cannot be preserved. Postoperatively, This finding was proved by the histopathological diagnosis which was invasive non-keratinized, moderately differentiated squamous cell carcinoma of the cervix (FIGO stage IB2).. patient was referred to oncologist for further management.

Adrenal tumors

Adrenal tumors Abstract. The term 'adrenal tumor' describes benign and malignant mass lesions of the adrenal gland, including primary adrenal tumors and metastases from extra-adrenal origin. With the widespread use of imaging technique, adrenal tumors have become increasingly detected as 'incidentalomas'. The detection of an adrenal tumor raises two questions: Is the mass malignant? Is the mass hormonally active? Whereas the evaluation for malignancy is based on specific imaging characteristics (imaging phenotype), a targeted clinical examination and specific biochemical tests are required to assess for hormonal secretion. An adrenal mass < 4 cm with clear benign features on imaging and with a normal hormonal workup does not require treatment. If malignancy is suspected further diagnostic procedures and / or adrenalectomy are indicated. For hormonally active tumors surgery is generally considered the treatment of choice; however, the decision for surgery has to be individualized for aldosterone-secreting tumors and for cortisol-secreting tumors with only mild cushing's syndrome. Also in patients with large tumors (> 4 cm), and in cases with non-conclusive evaluation for malignancy or hormonal activity, the decision for further management has to be made on an individual basis. A minimally invasive surgical approach may be considered in adrenal tumors < 6 cm and without local infiltration of adjacent structures. Both laparoscopic (transabdominal) and retroperitoneoscopic techniques are possible. The surgical outcome depends on the surgeon's experience. A close interdisciplinary collaboration is mandatory in the evaluation and treatment of adrenal tumors.

Multi-Scale Segmentation For Detecting Mass In Mammograms Using Deep Learning Techniques

This paper tends to the issue of fragmenting an image into the segment. Proposed system characterizes a predicate for estimating the proof for a limit between two districts utilizing a diagram-based portrayal of the picture. Proposed system at that point build up an efficient division calculation dependent on this predicate and demonstrate that although this calculation settles on ravenous choices it produces divisions that fulfill worldwide properties. Proposed system applies the calculation to picture division utilizing two different sorts of nearby neighbourhoods in building the chart and show the outcomes with both genuine and engineered pictures. The calculation keeps running in time about straight in the number of chart edges and is additionally quick by and by. A significant normal for the strategy is its capacity to safeguard detail in low-changeability picture districts while overlooking points of interest in high-fluctuation locales. Deep convolutional neural networks (CNNs) are investigated in the context of computer-aided diagnosis (CADx) of breast cancer. State-of-the-art CNNs are trained and evaluated on two mammographic datasets, consisting of ROIs depicting benign or malignant mass lesions. The performance evaluation of each examined network is addressed in two training scenarios: the first involves initializing the network with pre-trained weights, while for the second the networks are initialized in a random fashion. Extensive experimental results show the superior performance achieved in the case of fine-tuning a pretrained network compared to training from initial stages respectively. Our purpose is to develop a mammography-based DL breast cancer risk model that is more accurate than established clinical breast cancer risk models