Malignant Masses Research Articles

Study on Computed Tomography (CT) Guided Transthoracic Fine Needle Aspiration Cytology (FNAC) in the Diagnosis of Intrathoracic Mass Lesions - Not Approachable by USG

Background: Intrathoracic mass is a general issue encountered by the physicians in whole world including Bangladesh. Accurate diagnosis is important for proper management. Percutaneous fine needle aspiration cytology (FNAC) has been extensively utilized and widely accepted as an important means of diagnostic technique. This study was performed to know and justify the computed tomography (CT) guided FNAC as a diagnostic modality for the diagnosis of intrathoracic masses which are non-approachable by ultrasonography (USG). Methods: It was a descriptive type of cross sectional study and conducted among 30 patients having ultrasonologically non approachable intrathoracic masses during the period of Feb 2016 to July 2016 in Combined Military Hospital (CMH), Dhaka considering selection criteria and informed written consent using purposive sampling technique. FNAC was done in all the patients. After further investigation, a definitive diagnosis was made. The analysis of the surgical specimen, the biopsy, the therapeutic response, and the clinical follow-up all contributed to the ultimate diagnosis. A comparison was done between the cytological and final diagnosis for thirty cases. Ethical issues were properly addressed. Results: Initially CT guided FNAC was carried out on all patients. Conclusive cytological diagnosis was possible in 28 patients yielding 93.33% diagnostic accuracy. FNAC diagnosed 24 cases as malignant intrathoracic mass, 04 as benign lesion and 02 undiagnosed due to inadequate tissue material. In final diagnosis 25(75%) diagnosed as malignant and 05(25%) diagnosed as benign. Diagnosis of two patients was impossible by FNAC due to inadequate tissue material. Conclusion: CT guided FNAC of intrathoracic mass allow early diagnosis with lesser trauma and lower cost. This procedure avoided more invasive diagnostic surgery. Bangladesh Armed Forces Med J Vol 56 No (2) December 2023, pp 10-16

Role of diffusion weighted imaging with background body signal suppression (DWIBS) in diagnosis of breast masses and correlation with histopathological findings

BackgroundDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the important tool in breast imaging. However, two major limitations are represented by its specificity and by the injection of contrast material. Diffusion weighted imaging (DWI) provides important functional information without the need for contrast material. A newly introduced diffusion weighted imaging with background suppression (DWIBS) sequence is an accurate and rapid tool for the identification and characterization of breast lesions, with its short examination time, high lesion-to-background contrast and lack of need for intravenous contrast agents.ObjectiveTo assess the role of DWIBS sequence in the evaluation of indeterminate and suspicious breast masses and to compare its accuracy with DCE-MRI in correlation with histopathological findings.MethodsThirty-five patients were included in the study, referred from sono-mammography clinic to MRI unit for further MRI assessment of probably benign, suspicious and malignant looking breast masses (BIRADS 3, BIRADS 4 & BIRADS 5) on sono-mammography imaging results. MRI breast protocol which included DCE-MRI and DWIBS sequences were obtained for characterization and were verified by core needle biopsy or excisional biopsy. The results were statistically analyzed. Sensitivity, specificity, diagnostic accuracy (DA), positive predictive value (PPV) and negative predictive value (NPV) were calculated for DCE-MRI and DWIBS. Apparent diffusion co-efficient (ADC) values were calculated with ADC ≤ 1.2 × 10−3 mm2/s was considered suspicious for malignancy. The results were then compared with the histological findings.ResultsThirty-five female patients had 39 breast masses included in our study. By DCE-MRI, 8 (20.5%) masses were categorized as benign and 31(79.5%) masses were categorized as malignant. By DWIBS sequence, 7 (17.9%) masses were categorized as benign and 32 (82.1%) masses were categorized as malignant. By histopathology, 14 (35.9%) masses were benign and 25 (64.1%) masses were malignant. DCE-MRI obtained accuracy, sensitivity, specificity, PPV and NPV values of 84.6, 100, 57.1, 80.6 and 100%, respectively. DWIBS sequences obtained accuracy, sensitivity, specificity, PPV and NPV values of 82.1, 100, 50, 78.1and 100%, respectively.ConclusionsDWIBS can be added to DCE-MRI, as complementary tool to make radiologist more confident about the diagnosis. It can also be used instead of DCE-MRI sequences in certain circumstances such as in cases of renal impairment.

The Design of Machine Learning-Based Computer-Aided System with LabVIEW For Abnormalities in Mammogram Images

Mammogram is the best way of breast cancer detection nowadays, as breast cancer is the most common form of cancer in the female gender and this form of cancer usually causes death. Many scientists, doctors, and engineers are working together to deal with such serious issues in human life. This paper, it is aimed to develop a new computer-aided system with a graphical coded language to detect abnormalities in mammogram images by using machine learning technics such as ANN and SVM. The developed algorithm has a graphical user interface (GUI) and all results are shown in there. The algorithm was created using three different stages. These are image processing and mass segmentation, feature selection and extraction, and classification. To test the accuracy of the system as the sensitivity, specificity, and accuracy, mammogram images with forty benign and forty malignant masses were used. The obtained results for measuring the sensitivity, specificity, and accuracy are 95%, 97.5%, and 96.25% for ANN and 97.5%, 97.5, and 97.5 for SVM, respectively. As can be said that the algorithm, user-friendly due to its user interface, can be preferred because it can detect many cancerous cells such as breast cancer with high accuracy.

Comparing Sonazoid contrast-enhanced ultrasound to contrast-enhanced CT and MRI for differentially diagnosing renal lesions: a prospective multicenter study.

To evaluate the diagnostic performance of contrast-enhanced (CE) ultrasound using Sonazoid (SNZ-CEUS) by comparing with contrast-enhanced computed tomography (CE-CT) and contrast-enhanced magnetic resonance imaging (CE-MRI) for differentiating benign and malignant renal masses. 306 consecutive patients (from 7 centers) with renal masses (40 benign tumors, 266 malignant tumors) diagnosed by both SNZ-CEUS, CE-CT or CE-MRI were enrolled between September 2020 and February 2021. The examinations were performed within 7days, but the sequence was not fixed. Histologic results were available for 301 of 306 (98.37%) lesions and 5 lesions were considered benign after at least 2year follow-up without change in size and image characteristics. The diagnostic performances were evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and compared by McNemar's test. In the head-to-head comparison, SNZ-CEUS and CE-MRI had comparable sensitivity (95.60 vs. 94.51%, P = 0.997), specificity (65.22 vs. 73.91%, P = 0.752), positive predictive value (91.58 vs. 93.48%) and negative predictive value (78.95 vs. 77.27%); SNZ-CEUS and CE-CT showed similar sensitivity (97.31 vs. 96.24%, P = 0.724); however, SNZ-CEUS had relatively lower than specificity than CE-CT (59.09 vs. 68.18%, P = 0.683). For nodules > 4cm, CE-MRI demonstrated higher specificity than SNZ-CEUS (90.91 vs. 72.73%, P = 0.617) without compromise the sensitivity. SNZ-CEUS, CE-CT, and CE-MRI demonstrate desirable and comparable sensitivity for the differentiation of renal mass. However, the specificity of all three imaging modalities is not satisfactory. SNZ-CEUS may be a suitable alternative modality for patients with renal dysfunction and those allergic to gadolinium or iodine-based agents.

Enhancing surgical planning for abdominal tumors in children through advanced 3D visualization techniques: a systematic review of future prospects

IntroductionPreoperative three-dimensional (3D) reconstruction using sectional imaging is increasingly used in challenging pediatric cases to aid in surgical planning. Many case series have described various teams' experiences, discussing feasibility and realism, while emphasizing the technological potential for children. Nonetheless, general knowledge on this topic remains limited compared to the broader research landscape. The aim of this review was to explore the current devices and new opportunities provided by preoperative Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI).MethodsA systematic review was conducted to screen pediatric cases of abdominal and pelvic tumors with preoperative 3D reconstruction published between 2000 and 2023.DiscussionSurgical planning was facilitated through virtual reconstruction or 3D printing. Virtual reconstruction of complex tumors enables precise delineation of solid masses, formulation of dissection plans, and suggests dedicated vessel ligation, optimizing tissue preservation. Vascular mapping is particularly relevant for liver surgery, large neuroblastoma with imaging-defined risk factors (IDRFs), and tumors encasing major vessels, such as complex median retroperitoneal malignant masses. 3D printing can facilitate specific tissue preservation, now accessible with minimally invasive procedures like partial nephrectomy. The latest advancements enable neural plexus reconstruction to guide surgical nerve sparing, for example, hypogastric nerve modelling, typically adjacent to large pelvic tumors. New insights will soon incorporate nerve plexus images into anatomical segmentation reconstructions, facilitated by non-irradiating imaging modalities like MRI.ConclusionAlthough not yet published in pediatric surgical procedures, the next anticipated advancement is augmented reality, enhancing real-time intraoperative guidance: the surgeon will use a robotic console overlaying functional and anatomical data onto a magnified surgical field, enhancing robotic precision in confined spaces.

Leveraging a realistic synthetic database to learn Shape-from-Shading for estimating the colon depth in colonoscopy images

Colonoscopy is the choice procedure to diagnose, screening, and treat the colon and rectum cancer, from early detection of small precancerous lesions (polyps), to confirmation of malign masses. However, the high variability of the organ appearance and the complex shape of both the colon wall and structures of interest make this exploration difficult. Learned visuospatial and perceptual abilities mitigate technical limitations in clinical practice by proper estimation of the intestinal depth. This work introduces a novel methodology to estimate colon depth maps in single frames from monocular colonoscopy videos. The generated depth map is inferred from the shading variation of the colon wall with respect to the light source, as learned from a realistic synthetic database. Briefly, a classic convolutional neural network architecture is trained from scratch to estimate the depth map, improving sharp depth estimations in haustral folds and polyps by a custom loss function that minimizes the estimation error in edges and curvatures. The network was trained by a custom synthetic colonoscopy database herein constructed and released, composed of 248400 frames (47 videos), with depth annotations at the level of pixels. This collection comprehends 5 subsets of videos with progressively higher levels of visual complexity. Evaluation of the depth estimation with the synthetic database reached a threshold accuracy of 95.65%, and a mean-RMSE of 0.451cm, while a qualitative assessment with a real database showed consistent depth estimations, visually evaluated by the expert gastroenterologist coauthoring this paper. Finally, the method achieved competitive performance with respect to another state-of-the-art method using a public synthetic database and comparable results in a set of images with other five state-of-the-art methods. Additionally, three-dimensional reconstructions demonstrated useful approximations of the gastrointestinal tract geometry. Code for reproducing the reported results and the dataset are available at https://github.com/Cimalab-unal/ColonDepthEstimation.

The value of elastography strain rate ratio in benign and malignant BI-RADS-US 3-4 breast masses.

In the realm of breast diagnostics, Breast Imaging Reporting and Data System (BI-RADS) serves as a systematic framework, guiding a methodical exploration into the nuanced narratives of masses. The current study aims to investigate the value of strain rate ratio (SR) of elastography for benign and malignant breast masses categorized by BI-RADS for Ultrasonography (BI-RADS-US) category 3-4. Ultrasonographic data of 1099 breast masses that underwent both elastography and pathological examination were retrospectively analyzed by non-parametric test, consistency analysis and receiver operating characteristic (ROC) curve sequentially. ROC curve was used to evaluate the diagnostic effect of SR method in different BI-RADS categories of breast lesions. The maximum Youden index obtained from the ROC curve was 0.845. At a cut-off value of 3.57, the SR values' diagnostic sensitivity, specificity, accuracy, positive predictive value and negative predictive value for distinguishing benign and malignant breast lesions were 85.7 %, 98.8 %, 93.6 %, 97.9 % and 91.45 %, respectively. Consistency analysis showed that the consistency between SR value and pathological diagnosis was 93.6 % (κ = 0.864). In addition, the SR values between benign and malignant lesions of BI-RADS 3 and 4 were statistically different (P < 0.001). ROC analysis indicated that the diagnostic area under curve (AUC) for SR value in BI-RADS 3, 4a, 4b and 4c lesions were 0.985, 0.866, 0.793 and 0.916, respectively. In addition, the study observed 58 cases of missed diagnosis and 20 cases of misdiagnosis in evaluating benign and malignant breast lesions. The elastic SR ratio method has a good diagnostic value for the evaluation of breast masses, particularly for lesions with a score of 3-4. The elastic SR ratio method is instrumental in enhancing the accuracy of diagnosis.

Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance

ObjectiveIn physical health examinations, breast sonography is a commonly used imaging method, but it can lead to repeated exams and unnecessary biopsy due to discrepancies among radiologists and health centers. This study explores the role of off-the-shelf artificial intelligence (AI) software in assisting radiologists to classify incidentally found breast masses in two health centers.MethodsFemale patients undergoing breast ultrasound examinations with incidentally discovered breast masses were categorized according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS), with categories 3 to 5 included in this study. The examinations were conducted at two municipal health centers from May 2021 to May 2023.The final pathological results from surgical resection or biopsy served as the gold standard for comparison. Ultrasonographic images were obtained in longitudinal and transverse sections, and two junior radiologists and one senior radiologist independently assessed the images without knowing the pathological findings. The BI-RADS classification was adjusted following AI assistance, and diagnostic performance was compared using receiver operating characteristic curves.ResultsA total of 196 patients with 202 breast masses were included in the study, with pathological results confirming 107 benign and 95 malignant masses. The receiver operating characteristic curve showed that experienced breast radiologists had higher diagnostic performance in BI-RADS classification than junior radiologists, similar to AI classification (AUC = 0.936, 0.806, 0.896, and 0.950, p &amp;lt; 0.05). The AI software improved the accuracy, sensitivity, and negative predictive value of the adjusted BI-RADS classification for the junior radiologists’ group (p&amp;lt; 0.05), while no difference was observed in the senior radiologist group. Furthermore, AI increased the negative predictive value for BI-RADS 4a masses and the positive predictive value for 4b masses among radiologists (p &amp;lt; 0.05). AI enhances the sensitivity of invasive breast cancer detection more effectively than ductal carcinoma in situ and rare subtypes of breast cancer.ConclusionsThe AI software enhances diagnostic efficiency for breast masses, reducing the performance gap between junior and senior radiologists, particularly for BI-RADS 4a and 4b masses. This improvement reduces unnecessary repeat examinations and biopsies, optimizing medical resource utilization and enhancing overall diagnostic effectiveness.

Breast imaging reporting and data system for sonography: Positive and negative predictive values of sonographic features in Kumasi, Ghana

BackgroundBreast cancer is the most common female cancer globally. The method of choice for screening and diagnosing breast cancer is mammography, which is not widely available in Ghana as compared to ultrasonography. This study aimed to evaluate the sonographic features of solid breast lesions using the new sonographic Breast Imaging- Reporting and Data System (BI-RADS-US) lexicon for malignancy with histopathology as the gold standard. MethodsThis was a prospective quantitative study that sonographically scanned female patients with breast masses and consecutively selected cases recommended for core biopsy from May 2018 to May 2021. Sixty (60) solid breast masses were described using the sonographic BI-RADS lexicon features. Lesion description and biopsy results from histopathology were compared and analyzed using Pearson's Chi-square test. Odds ratios, sensitivity, specificity, and predictive values were also calculated. Statistical significance level was set at p ≤ 0.05. ResultsIrregular shape (p < 0.0001), spiculated mass margins (p < 0.0001), and not parallel mass orientation (p= 0.0007) were more commonly associated with malignant masses. The sensitivity of breast ultrasound for malignancy was 93.9 % and the specificity was 55.6 % with an overall accuracy rate of 76.6 %. The negative predictive value was 88.7 % and the positive predictive value was 72.1 %. Descriptors like irregular shape, non-parallel orientation, angular and spiculated margins, echogenic halo, and markedly hypoechoic internal content, demonstrated higher odds ratios for malignancy. ConclusionsThis study adds valuable insights to the diagnosis of breast cancer using the sonographic BI-RADS lexicon features. The results demonstrate that specific sonographic descriptors can effectively differentiate between benign and malignant breast masses.

Effect of contrast agent on T2-weighted fat-suppressed imaging and diffusion-weighted imaging in the diagnosis of breast tumors.

Although previous studies have shown that the injection of contrast agents can improve image quality, the specific impact of this on T2-weighted fat-suppressed (T2 FS) and diffusion-weighted imaging (DWI) sequences in the diagnosis of breast cancer remains incompletely understood. In particular, there is insufficient research on how contrast agents affect the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values within these sequences, and how these changes influence the diagnosis of benign and malignant breast tumors. Breast magnetic resonance images (MRI) were obtained from 178 consecutive patients on a 3T scanner. The SNR and CNR of lesions on T2 FS sequence were calculated before and after contrast agent injection and compared. Differences between pre- and post-contrast ADC in identifying different tumor types were compared using the Kruskal-Wallis H-test and the paired comparison test. The accuracy of ADC values between pre- and post-contrast in distinguishing benign and malignant breast masses was assessed using receiver operating characteristic (ROC) curves. The SNR and CNR of T2 FS sequence increased after contrast injection, and especially for invasive cancer and benign tumor, the increase was significant. For DWI, there was a slight increase or decrease of ADC values after contrast injection, but the ADC values before and after contrast had a similar effect in identifying different types of tumors. In the ROC curve analysis for assessing benign and malignant breast tumors, the area under the curve (AUC) before and after contrast showed similar results. Contrast agent injection can improve the SNR and CNR of T2 FS sequence, thus providing higher quality images for the diagnosis of breast lesions. Furthermore, injection of contrast agent had little effect on the ability of ADC values to identify different types of lesions and both ADC values before and after the contrast agent were able to distinguish between benign and malignant tumors with almost the same accuracy.

Assessing the Use of the Triglyceride-Glycemic Index (TyG), Neutrophil-Lymphocyte Ratio (NLR), and Platelet-Lymphocyte Ratio (PLR) in Distinguishing Benign and Malignant Tumors Among Patients With Complaints of Breast Mass

IntroductionBreast cancer is a prevalent global health concern characterized by uncontrolled cell growth in breast tissue. In 2020, approximately 2.3 million cases were reported worldwide, with 162,468 new cases and 87,090 fatalities documented in India in 2018. Early diagnosis is crucial for reducing mortality. Our study focused on the use of markers such as the triglyceride-glycemic index and hematological markers to distinguish between benign and malignant breast masses. MethodsA prospective cross-sectional study included female patients with breast mass complaints. The target sample size was 200. Data collection included medical history, clinical breast examination, mammography, cytological assessment via fine-needle aspiration cytology (FNAC), and blood sample collection. The analyzed parameters included neutrophil-to-lymphocyte Ratio (NLR), platelet-to-lymphocyte Ratio (PLR), and triglyceride-glycemic index (TyG). Histopathological examination confirmed the FNAC results. Statistical analysis including propensity score matching, Kolmogorov–Smirnov tests, Mann–Whitney U tests, receiver's operator curve (ROC) analysis, and logistic regression models was conducted using SPSS and R Software. Additional validation was performed on 25 participants. ResultsThis study included 200 participants. 109 had benign tumors and 91 had malignant tumors. Propensity score matching balanced covariates. NLR did not significantly differ between the groups, while PLR and TyG index differed significantly. NLR correlated strongly with the breast cancer stage, but not with the BI-RADS score. PLR and TyG index showed moderate positive correlations with the BI-RADS score. ROC analysis was used to determine the optimal cutoff values for PLR and TyG index. Logistic regression models combining PLR and TyG index significantly improved malignancy prediction. ConclusionsTyG index and PLR show potential as adjunctive markers for distinguishing breast masses. NLR correlated with cancer stage but not lesion type. Combining TyG and PLR improves prediction, aiding clinical decisions, but large-scale multicenter trials and long-term validation are required for clinical implementation.

A rare case malignant granular cell tumor of the thigh with radiological imaging likely rhabdomyosarcoma: A case report

Granular cell tumors (GCTs) are uncommon soft tissue tumors characterized by the cytoplasmic granular appearance of the neoplastic cells. Malignant GCTs are rare neural tumors, intensely aggressive. Both the rare occurrence of malignant GCTs and its similarities in features with their benign lesions make the diagnosis of this malignancy difficult. Malignant GCTs comprise less than 2% of GCTs and are mostly found in the subcutaneous soft tissues of the lower extremities, especially the thighs. This article presents a case of a large malignant GCTs in the left thigh of a 46-year-old woman. Plain radiograph of the left thigh demonstrated a soft tissue mass without bone involvement. MRI images showed a malignant solid mass 7.8 x 5.2 x 21.6 cm, with ill-defined border, irregular edges, and suspected rhabdomyosarcoma. Incisional biopsy of the mass lesion revealed mixed epithelioid and spindle tumors, suspected malignant GCTs. Histological examination performed by a musculoskeletal pathologist, demonstrated nests and sheets of epithelioid to polygonal cells with hyperchromatic, intensely eosinophilic granular cytoplasm, prominent nucleoli, increased mitotic activity, necrosis, areas of spindling with significant atypia, and positive for S100 by immunohistochemistry. In conclusion, this patient with malignant soft tissue tumor demonstrated polygonal and spindle cells with eosinophilic granular cytoplasm, which need to be considered diagnosed as Malignant GCTs. This case stresses the importance of thorough histological examination in the diagnosis of malignant GCTs.

MRI Accuracy in Diagnosing Sonographically Indeterminate Masses Taking Histopathology as Standard

Introduction: Approximately 25% of ultrasound-detected adnexal masses pose clinical challenges, being indeterminately categorized as benign or malignant. Characterizing them is pivotal for deciding surgery or pelvic MRI. Aims and Objectives: To validate the accuracy of MRI for diagnosing sonographically indeterminate masses using biopsy as the gold standard. Place and Duration of study: A validation cross-sectional study was performed at the Department of Radiology, Mayo Hospital, Lahore, for a period of six months i.e. from December 2019 to June 2020. Material and Methods: Non-probability consecutive sampling was employed to select 289 patients (12-60 years) with sonographically indeterminate adnexal masses. All those patients who were unwilling to participate or had MRI contraindications like metallic inserts, pacemakers, and claustrophobia were excluded from the study. Data was collected using the proforma as approved by IRB. All patients underwent MR imaging on a 1.5-T GE unit, and MRI accuracy was calculated. The analysis of data was performed using SPSS 25.0 version software, p-value ? 0.05 was taken as significant. Results: MRI sensitivity, specificity, PPV, NPV, FP, FN, and diagnostic accuracy in sonographically inconclusive adnexal lesions were 94.25%, 85.22%, 90.61%, 90.74%, 3.46%, 5.88% and 90.66%, respectively, referencing histopathology. Conclusion: The study concludes MRI as a noninvasive, accurate modality for distinguishing benign and malignant adnexal masses. While IOTA Simple Rules can't categorize all masses, around 20% with inconclusive results may need alternative evaluation, like skilled ultrasound examination&amp; MRI. It significantly enhances preoperative differentiation, aiding surgeons in making informed decisions regarding treatment approaches.

Role of Sonoelastography in Differentiating Benign and Malignant Breast Lesions: A Prospective Study Comparing Elasticity Contrast Index and Tsukuba Score

Introduction: Breast cancer is the most common cancer occurring in women globally which is potentially curable if detected early. Ultrasound elastography is a dynamic technique that estimates tissue stiffness to differentiate between benign and malignant masses. Our study aimed to determine and compare the diagnostic accuracy of B mode Sonography, Elasticity contrast index, and Tsukuba score in differentiating malignant and benign breast masses. Methods: This was a prospective cross-sectional study done including 110 lesions in 102 patients in the age group of 15-73 years. The solid breast lesions seen on sonography were categorized according to the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) and further evaluated with elastography using both Elasticity Contrast Index (ECI) and Tsukuba score with pathological diagnosis taken as the gold standard. The cut-off value of ECI was obtained. The diagnostic accuracy of B-mode sonography, ECI, and Tsukuba score was compared. Results: We found that B-mode sonography had a sensitivity of 85.7%, specificity of 100%, and accuracy of 96.9 %. The accuracy of Tsukuba scores for differentiating benign and malignant lesions was 81.2%. The cut-off value of ECI obtained was 2.8 and the accuracy was 81.8%. A statistically significant correlation (p &lt; 0.05) existed between sonographic diagnosis, ECI, and Tsukuba score. Conclusions: B mode sonography had the highest diagnostic accuracy while ECI and Tsukuba scores were comparable. ECI can be used for breast masses using a cut-off of 2.8 to differentiate benign from malignant.

Research progress on ultrasound medicine in diagnosis and evaluation of chemotherapy effect of breast cancer

Breast cancer has the highest incidence rate among cancers in the world and is the main cause of death among female cancer patients around the world. The progress of ultrasound technology has continuously improved the ability to identify the shape and microvessels of breast masses. It plays an important role in the differentiation of benign and malignant breast masses and is the main auxiliary diagnostic method for breast cancer. The widespread implementation of neoadjuvant chemotherapy for breast cancer has improved the survival rate of patients. Evaluating the efficacy after chemotherapy and adjusting chemotherapy strategies as needed can help improve the prognosis of patients. Ultrasound can conduct multi-parameter measurements of breast tumor hardness, blood flow velocity and vascular distribution, which can be applied to evaluate the efficacy of neoadjuvant chemotherapy for breast cancer. This article reviewed the application and research progress of multi-modal ultrasound in the diagnosis and evaluation of chemotherapy efficacy of breast cancer.

Diagnosing and staging epithelial ovarian cancer by serum glycoproteomic profiling.

There is a need for diagnostic tests for screening, triaging and staging of epithelial ovarian cancer (EOC). Glycoproteomics of blood samples has shown promise for biomarker discovery. We applied glycoproteomics to serum of people with EOC or benign pelvic masses and healthy controls. A total of 653 analytes were quantified and assessed in multivariable models, which were tested in an independent cohort. Additionally, we analyzed glycosylation patterns in serum markers and in tissues. We identified a biomarker panel that distinguished benign lesions from EOC with sensitivity and specificity of 83.5% and 90.1% in the training set, and of 86.7 and 86.7% in the test set, respectively. ROC analysis demonstrated strong performance across a range of cutoffs. Fucosylated multi-antennary glycopeptide markers were higher in late-stage than in early-stage EOC. A comparable pattern was found in late-stage EOC tissues. Blood glycopeptide biomarkers have the potential to distinguish benign from malignant pelvic masses, and early- from late-stage EOC. Glycosylation of circulating and tumor tissue proteins may be related. This study supports the hypothesis that blood glycoproteomic profiling can be used for EOC diagnosis and staging and it warrants further clinical evaluation.

Attention-map augmentation for hypercomplex breast cancer classification

Breast cancer is the most widespread neoplasm among women and early detection of this disease is critical. Deep learning techniques have become of great interest to improve diagnostic performance. However, distinguishing between malignant and benign masses in whole mammograms poses a challenge, as they appear nearly identical to an untrained eye, and the region of interest (ROI) constitutes only a small fraction of the entire image. In this paper, we propose a framework, parameterized hypercomplex attention maps (PHAM), to overcome these problems. Specifically, we deploy an augmentation step based on computing attention maps. Then, the attention maps are used to condition the classification step by constructing a multi-dimensional input comprised of the original breast cancer image and the corresponding attention map. In this step, a parameterized hypercomplex neural network (PHNN) is employed to perform breast cancer classification. The framework offers two main advantages. First, attention maps provide critical information regarding the ROI and allow the neural model to concentrate on it. Second, the hypercomplex architecture has the ability to model local relations between input dimensions thanks to hypercomplex algebra rules, thus properly exploiting the information provided by the attention map. We demonstrate the efficacy of the proposed framework on both mammography images as well as histopathological ones. We surpass attention-based state-of-the-art networks and the real-valued counterpart of our approach. The code of our work is available at https://github.com/ispamm/AttentionBCS.

Comparison of conventional diffusion-weighted imaging, diffusion kurtosis imaging and intravoxel incoherent motion in characterization of sonographically indeterminate adnexal masses.

To evaluate the role of conventional diffusion weighted imaging, diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) in distinguishing benign from malignant adnexal masses. 38 patients with 45 adnexal masses were enrolled in this prospective study and assessed with multiparametric MRI, including the IVIM-DKI sequence, on a 3T MRI system. The mean apparent diffusion coefficient (ADC) from conventional DWI, the apparent diffusion coefficient derived from DKI (Dapp), the apparent kurtosis coefficient (Kapp), true diffusion coefficient (Dt), perfusion fraction (f) and pseudo-diffusion coefficient (Dp) were measured. The mean ADC, Dapp, and Dt were significantly higher in benign adnexal masses than in malignant adnexal masses (p < 0.001). f and Dp were also significantly higher in benign adnexal masses, with p values of 0.026 and 0.002, respectively. Kapp was higher in malignant masses (p < 0.001). Among mean ADC, Dapp, and Dt, mean ADC had the highest area under the curve (AUC) of 0.885. However, no statistically significant differences were observed between the ROCs of various diffusion parameters. The mean ADC, Dapp, and Kapp are useful parameters in discriminating between benign and malignant adnexal masses. Dt derived from IVIM also helps in distinguishing benign and malignant adnexal masses; however, no incremental role of IVIM and DKI over ADC could be identified in our study.

Ultrasound-based radiomics for the differential diagnosis of breast masses: A systematic review and meta-analysis.

Ultrasound-based radiomics has demonstrated excellent diagnostic performance in differentiating benign and malignant breast masses. Given a few clinical studies on their diagnostic role, we conducted a meta-analysis of the potential effects of ultrasound-based radiomics for the differential diagnosis of breast masses, aiming to provide evidence-based medical basis for clinical research. We searched Embase, Web of Science, Cochrane Library, and PubMed databases from inception through to February 2023. The methodological quality assessment of the included studies was performed according to Quality Assessment of Diagnostic Accuracy Studies checklist. A diagnostic test accuracy systematic review and meta-analysis was performed in accordance with PRISMA guidelines. Sensitivity, specificity, and area under curve delineating benign and malignant lesions were recorded. We also used sensitivity analysis and subgroup analysis to explore potential sources of heterogeneity. Deeks' funnel plots was used to examine the publication bias. A total of 11 studies were included in this meta-analysis. For the diagnosis of malignant breast masses worldwide, the overall mean rates of sensitivity and specificity of ultrasound-based radiomics were 0.90 (95% confidence interval [CI], 0.83-0.95) and 0.89 (95% CI, 0.82-0.94), respectively. The summary diagnostic odds ratio was 76 (95% CI, 26-219), and the area under the curve for the summary receiver operating characteristic curve was 0.95 (95% CI, 0.93-0.97). Ultrasound-based radiomics has the potential to improve diagnostic accuracy to discriminate between benign and malignant breast masses, and could reduce unnecessary biopsies.

Feasibility of Amide Proton Transfer-Weighted Imaging at 3T for Renal Masses: A Preliminary Study.

To investigate the optimal B1,rms value of renal amide proton transfer-weighted (APTw) images and the reproducibility of this value, and to explore the utility of APT imaging of renal masses and kidney tissues. APTw images with different B1,rms values were repeatedly recorded in 15 healthy volunteers to determine the optimal value. Two 4-point Likert scales (poor [1] to excellent [4]) were used to evaluate contour clarity and artifacts in masses and normal tissues. The APTw values of masses and normal tissues were then compared in evaluable images (contour clarity score > 1, artifacts score > 1). The APTw of malignant masses, normal tissues, and benign masses were calculated and compared with the Mann-Whitney U test. The optimal scanning parameter of B1,rms was 2 μT, and the APTw images had good agreement in the volunteers. Our study of APTw imaging examined 70 renal masses (13 benign, 57 malignant) and 49 normal kidneys (including those from 15 healthy volunteers). The mean APTw value for renal malignant masses (2.28(1.55)) was different from that for benign masses (0.91(1.30)) (P<0.001), renal cortex (1.30 (1.25)) (P<0.001), renal medulla (1.64 (1.33)) (P<0.05), and renal pelvis (5.49 (2.65)) (P<0.001). These preliminary data demonstrate that APTw imaging of the kidneys has potential use as an imaging biomarker for the differentiation of normal tissues, malignant masses, and benign masses.