Malignant Lesions Research Articles

Profile and Prevalence of Oral and Maxillofacial Diseases Among Armed Forces Personnel and Their Families in Military Dental Centre, Cumilla

Background: Oral and maxillofacial diseases encompass a wide range of conditions in and around oral cavity and in maxillofacial region. It includes maxillofacial trauma, cyst and tumour of jaw bones, conditions involving impacted wisdom teeth, oral premalignant and malignant lesions, fascial space infection around maxillofacial region, temporomandibular joint disorder, dental implantology, salivary gland disorder and congenital defects of jaw bones. Maxillofacial region is more vulnerable to sustain injuries in the battlefield. In peace time, various maxillofacial diseases reduce the overall performances of the troops. The aim of this study is to delineate rank wise distribution of armed forces personnel reported with maxillofacial diseases, gender distribution, types of maxillofacial diseases, different types of surgical procedures performed and types of anesthesia used. Methods: A descriptive cross-sectional study was conducted to assess the prevalence of maxillofacial diseases among the armed forces personnel and their entitled family members during the period of 1st January 2019 to 31st December 2019 in Military Dental Centre, Cumilla. Purposive sampling technique and semi structured questionnaire was used to include the patients in this study.Data were analyzed by using Statistical Packages for Social Sciences (SPSS) version 25. Results: A total of 170 cases were evaluated and treated. Male patients were 110(65%) and female were 60(35%). Among the military personnel numbers of sainik (privates) were highest 32(35%). Impacted wisdom teeth cases were mostly found 80(47%). In case of impacted teeth mesio angular impactions were most 35(35%). In case of maxillofacial pathological conditions cystic lesions were most in number 22(39%) (n=56). Maxillofacial soft tissue laceration cases were highest 05(29%) (n=17) among all trauma patients. Surgical extraction of wisdom teeth were mostly performed surgery 80(59%) (n=138). In regards to administration of anesthesia, 128(93%) cases were performed under local anesthesia and 10(7%) were under general anesthesia. Conclusion: The present study imparts valuable perception about the types and prevalence of maxillofacial diseases and the treatment modalities among the armed forces personnel and their families in Cumilla area. Bangladesh Armed Forces Med J Vol 56 No (2) December 2023, pp 24-30

Sample strategies for the assessment of the apparent diffusion coefficient in single large intracranial space-occupying lesions of dogs and cats.

Diffusion-weighted imaging is increasingly available for brain investigation. Image interpretation of intracranial space-occupying lesions often includes the derived apparent diffusion coefficient (ADC) analysis. In human medicine, ADC can help discriminate between benign and malignant lesions in intracranial tumors. This study investigates the difference in ADC values depending on the sample strategies of image analysis. MRI examination, including diffusion-weighted images of canine and feline patients presented between 2015 and 2020, were reviewed retrospectively. Patients with single, large intracranial space-occupying lesions were included. Lesions homogeneity was subjectively scored. ADC values were calculated using six different methods of sampling (M1-M6) on the ADC map. M1 included as much as possible of the lesion on a maximum of five consecutive slices; M2 included five central and five peripheral ROIs; M3 included a single ROI on the solid part of the lesion; M4 included three central ROIs on one slice; M5 included three central ROIs on different slices; and M6 included one large ROI on the entire lesion. A total of 201 animals of various breeds, genders, and ages were analyzed. ADC values differed significantly between M5 against M2 (peripheral) (p < 0.001), M5 against M6 (p = 0.009), and M4 against M2 (peripheral) (p = 0.005). When lesions scored as homogeneous in all sequences were excluded, an additional significant difference in three further sampling methods was present (p < 0.005). ADC of single, large, intracranial space-occupying lesions differed significantly in half of the tested methods of sampling. Excluding homogeneous lesions, additional significant differences among the sampling methods were present. The obtained results should increase awareness of the variability of the ADC, depending on the sample strategies used.

Validity of dynamic contrast-enhanced magnetic resonance imaging of the breast versus diffusion-weighted imaging and magnetic resonance spectroscopy in predicting the malignant nature of non-mass enhancement lesions

BackgroundBreast cancer is the commonest cancer affecting women worldwide. So, it is important to accurately detect and classify different breast lesions. Noninvasive methods for tissue characterization have increased interest, particularly for early diagnosis. Non-mass enhancement (NME) breast lesions are described in magnetic resonance imaging (MRI) as the presence of enhancement without space-occupying lesions. Several studies have described that certain characteristics can be used as new indicators of malignancy in breast NME lesions. We aimed to study the role of multiparametric-MRI (Mp-MRI) as diffusion-weighted imaging (DWI) and magnetic resonance spectroscopy (MRS) in assessment of NME lesions and to suggest which one offers the greatest diagnostic accuracy.MethodsThis retrospective study was conducted from March 2017 to December 2023 on 220 NME breast lesions. All lesions were analyzed to study the features of benign and malignant NME lesions using different MRI techniques including dynamic contrast-enhanced MRI (DCE-MRI), DWI, and MRS. Breast MRI was performed at 1.5 Tesla, findings were correlated with histopathological results of all cases.ResultsPatients’ mean age was 46.56 years with 220 NME breast lesions (54 were benign and 166 were malignant). Invasive ductal carcinoma with ductal carcinoma in situ was the most malignant type representing 93 cases. We found that segmental distribution, heterogeneous enhancement, type III curve, restricted diffusion, lower apparent diffusion coefficient, and positive choline peak were more with malignancy (P = 0.008, 0.02, 0.004, 0.001, and < 0.001). We detected that Mp-MRI has higher diagnostic accuracy than DCE-MRI and combined other functional sequences (DWI, MRS), it was 91.2% with sensitivity 89.9%, specificity 87.8%, positive predictive value 89.2%, and negative predictive value 82.2%.ConclusionsFunctional MRI techniques, such as DWI and MRS, can provide helpful information in assessment of NME lesions. They have high diagnostic accuracy, sensitivity, and specificity in characterizing NME breast lesions as benign or malignant. However, DCE-MRI is mandatory for lesion characterization and delineation of its nature and cannot be replaced by them alone in cases of lesion visualization. So, multiparametric-MRI can improve the diagnostic accuracy of NME breast lesions when combined with dynamic contrast-enhanced MRI and can help in reducing negative biopsy rates.

Evaluation of Candidal Species in Oral Submucous Fibrosis and Oral Lichen Planus: A Microbiological Study

Background: Candida is opportunistic microorganisms in oral cavity, causes oral candidiasis. The presence of candida and subspecies in oral premalignant lesions may intensify the infection and transform pre malignant lesions into cancer. Objective: To isolate various types of candida species, among Oral submucous fibrosis (OSMF) and Oral lichen Planus (OLP) patients. Methodology: Total 60 individuals included and grouped as; OSMF (n=20) group A, Lichen Planus (n=10); group B and Controls (n=30) group C. Results: The higher candida carriage revealed in OSMF+ OLP groups (23.3%) as compared to controls (10%). The species level findings showed 13.3% of candida albicans in OSMF+ OLP and 6.7% in controls, candida krusei 6.7% in OSMF+ OLP and 3.3% in controls and non candida albicans were 10% in OSMF+ OLP and 6.7% in control group respectively. Conclusion: This basic understanding of candida species and their association with potentially malignant disorders will help in better interventions, so as to restrict the lesion before it transform in to malignancy.

Multiparametric approach with synthetic MR imaging for diagnosing salivary gland lesions.

To determine whether synthetic MR imaging can distinguish between benign and malignant salivary gland lesions. The study population included 44 patients with 33 benign and 11 malignant salivary gland lesions. All MR imaging was obtained using a 3 Tesla system. The QRAPMASTER pulse sequence was used to acquire images with four TI values and two TE values, from which quantitative images of T1 and T2 relaxation times and proton density (PD) were generated. The Mann-Whitney U test was used to compare T1, T2, PD, and ADC values among the subtypes of salivary gland lesions. ROC analysis was used to evaluate diagnostic capability between malignant tumors (MTs) and either pleomorphic adenomas (PAs) or Warthin tumors (WTs). We further calculated diagnostic accuracy for distinguishing malignant from benign lesions when combining these parameters. PAs demonstrated significantly higher T1, T2, PD, and ADC values than WTs (all p < 0.001). Compared to MTs, PAs had significantly higher T1, T2, and ADC values (all p < 0.001), whereas WTs had significantly lower T1, T2, and PD values (p < 0.001, p = 0.008, and p = 0.003, respectively). T2 and ADC were most effective in differentiating between MTs and PAs (AUC = 0.928 and 0.939, respectively), and T1 and PD values for differentiating between MTs and WTs (AUC = 0.915 and 0.833, respectively). Combining T1 with T2 or ADC achieved accuracy of 86.4% in distinguishing between malignant and benign tumors. Similarly, combining PD with T2 or ADC reached accuracy of 86.4% for differentiating between malignant and benign tumors. Utilizing a combination of synthetic MRI parameters may assist in differentiating malignant from benign salivary gland lesions.

Current Preoperative Management of Vulvar Squamous Cell Carcinoma: An Overview.

Vulvar carcinoma is a rare cancer affecting the genital tract, constituting 4% of gynecological tumors. Vulvar squamous cell carcinoma (VSCC) is the most common type. Diagnosis relies on biopsy during vulvoscopy, plus imaging such as ultrasonography (USG), magnetic resonance imaging (MRI) and positron emission tomography (PET). This review aims to lay out a thorough overview as to the current preoperative management of VSCC, both in case of vulvar and lymph node involvement. The data research was conducted using the following databases: MEDLINE, EMBASE, Web of Sciences, Scopus, ClinicalTrial.gov, OVID and Cochrane Library from 2010 to 2024. The selection criteria included only original articles. Seventeen studies were assessed for eligibility. A concordance rate of 62.3% for vHSIL and 65.2% for carcinoma at vulvoscopy, with a sensitivity of 98%, specificity of 40%, PPV (Positive Predictive Value) of 37% and NPV (Negative Predictive Value) of 98% in identifying malignant lesions was found. Regarding the reliability of PET for staging and assessing lymph node involvement, a mean SUV (Standardized Uptake Value) for malignant vulvar lesions of 8.4 (range 2.5-14.7) was reported. In the case of MRI, useful for the evaluation of loco-regional infiltration and lymph node involvement, the ratio of the short-to-long-axis diameter and the reader's diagnostic confidence for the presence of lymph node metastasis yielded accuracy of 84.8% and 86.9%, sensitivity of 86.7% and 87.5%, specificity of 81.3% and 86.2%, PPV of 89.7% and 87.5% and NPV of 76.5% and 86.2%, respectively. A long lymph node axis >10 mm and a short diameter >5.8 mm were found to be predictors of malignancy. At USG, instead, the two main characteristics of potentially malignant lymph nodes are cortical thickness and short axis length; the combination of these ultrasound parameters yielded the highest accuracy in distinguishing between negative and positive lymph nodes. Despite the heterogeneity of the included studies and the lack of randomized clinical trials, this review provides a broad overview of the three imaging tools used for the presurgical management of VSCC. Nowadays, although MRI and PET represent the gold standard, ultrasound evaluation is taking on a growing role, as long as it is carried out by expert sonographer. The management of this rare disease should be always performed by a multidisciplinary team in order to precisely stage the tumor and determine the most suitable treatment approach.

BRAIxDet: Learning to detect malignant breast lesion with incomplete annotations

Methods to detect malignant lesions from screening mammograms are usually trained with fully annotated datasets, where images are labelled with the localisation and classification of cancerous lesions. However, real-world screening mammogram datasets commonly have a subset that is fully annotated and another subset that is weakly annotated with just the global classification (i.e., without lesion localisation). Given the large size of such datasets, researchers usually face a dilemma with the weakly annotated subset: to not use it or to fully annotate it. The first option will reduce detection accuracy because it does not use the whole dataset, and the second option is too expensive given that the annotation needs to be done by expert radiologists. In this paper, we propose a middle-ground solution for the dilemma, which is to formulate the training as a weakly- and semi-supervised learning problem that we refer to as malignant breast lesion detection with incomplete annotations. To address this problem, our new method comprises two stages, namely: (1) pre-training a multi-view mammogram classifier with weak supervision from the whole dataset, and (2) extending the trained classifier to become a multi-view detector that is trained with semi-supervised student–teacher learning, where the training set contains fully and weakly-annotated mammograms. We provide extensive detection results on two real-world screening mammogram datasets containing incomplete annotations and show that our proposed approach achieves state-of-the-art results in the detection of malignant breast lesions with incomplete annotations.

Retrospective analysis of thyroid incidentalomas detected by [68Ga]Ga-PSMA-11 PET/CT.

Prostate cancer patients, undergo imaging procedures, with [68Ga]Ga-PSMA-11 PET/CT (prostate-specific membrane antigen based positron emission tomography/computed tomography) utilized for primary and secondary staging. PSMA thyroid incidentalomas (PTI) are discovered in the thyroid gland while imaging prostate cancer patients with [68Ga]Ga-PSMA-11 PET/CT. The aim of the study was to determine the clinical significance of PTIs detected on [68Ga]Ga-PSMA-11 PET/CT. Another goal was to identify a possible threshold for the maximum standardized uptake value (SUVmax), above which a malignant growth could be suspected. A retrospective cross-sectional study. 769 patients with prostat cancer who underwent [68Ga]Ga-PSMA-11 PET/CT scans in the nuclear medicine department of a tertiary care hospital between January 2020 and December 2022 were retrospectively screened in this study. We analyzed 67 patients in whom PTI was detected. Patients who exceeded the inclusion criteria had their thyroid ultrasonography and ultrasonography -guided fine needle aspiration findings analyzed. PTI was discovered in 67 patients (8%). 42 patients who met the inclusion and exclusion criteria were included in the study. Of the 4 malignant patients (9.5%) in the study population, 2 were classified as TIRADS 3 and 2 were classified as TIRADS 4. The cut-off SUVmax value was found to be 5.6. With 100% sensitivity and 47.37% specificity, a cutoff SUVmax of 5.3 was determined through receiver-operator characteristic analysis in order to predict malignant cytology. PTI is a significant clinical finding; most of diffuse and focal uptakes are frequently related to benign diseases. Each center should establish its own a possible SUVmax cut-off over which a malignant lesion should be suspected.

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

Objectives18F-fluorodeoxyglucose (FDG) PET/CT has been widely used for the differential diagnosis of cancer. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may make it difficult to differentiate between benign and malignant lesions. It is crucial to find reliable quantitative metabolic parameters to further support the diagnosis. This study aims to evaluate the value of the quantitative metabolic parameters derived from dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting epidermal growth factor receptor (EGFR) mutation status.MethodsWe included 147 patients with lung lesions to perform FDG PET/CT dynamic plus static imaging with informed consent. Based on the results of the postoperative pathology, the patients were divided into benign/malignant groups, adenocarcinoma (AC)/squamous carcinoma (SCC) groups, and EGFR-positive (EGFR+)/EGFR-negative (EGFR-) groups. Quantitative parameters including K1, k2, k3, and Ki of each lesion were obtained by applying the irreversible two-tissue compartmental modeling using an in-house Matlab software. The SUV analysis was performed based on conventional static scan data. Differences in each metabolic parameter among the group were analyzed. Wilcoxon rank-sum test, independent-samples T-test, and receiver-operating characteristic (ROC) analysis were performed to compare the diagnostic effects among the differentiated groups. P < 0.05 were considered statistically significant for all statistical tests.ResultsIn the malignant group (N = 124), the SUVmax, k2, k3, and Ki were higher than the benign group (N = 23), and all had-better performance in the differential diagnosis (P < 0.05, respectively). In the AC group (N = 88), the SUVmax, k3, and Ki were lower than in the SCC group, and such differences were statistically significant (P < 0.05, respectively). For ROC analysis, Ki with cut-off value of 0.0250 ml/g/min has better diagnostic specificity than SUVmax (AUC = 0.999 vs. 0.70). In AC group, 48 patients further underwent EGFR testing. In the EGFR (+) group (N = 31), the average Ki (0.0279 ± 0.0153 ml/g/min) was lower than EGFR (-) group (N = 17, 0.0405 ± 0.0199 ml/g/min), and the difference was significant (P < 0.05). However, SUVmax and k3 did not show such a difference between EGFR (+) and EGFR (-) groups (P>0.05, respectively). For ROC analysis, the Ki had a cut-off value of 0.0350 ml/g/min when predicting EGFR status, with a sensitivity of 0.710, a specificity of 0.588, and an AUC of 0.674 [0.523–0.802].ConclusionAlthough both techniques were specific, Ki had a greater specificity than SUVmax when the cut-off value was set at 0.0250 ml/g/min for the differential diagnosis of lung cancer. At a cut-off value of 0.0350 ml/g/min, there was a 0.710 sensitivity for EGFR status prediction. If EGFR testing is not available for a patient, dynamic imaging could be a valuable non-invasive screening method.

Endobronchial ultrasound-guided transbronchial needle aspiration for diagnosing thoracic lesions: a retrospective cohort study.

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a minimally invasive technique for biopsy of lung, peri-pulmonary tissue and lymph nodes under real-time ultrasound-guided biopsy. It is used in the diagnosis and/or staging of benign and malignant pulmonary and non-pulmonary diseases. Our study is based on a large sample size, in a diversified population which provides a representative real-world cohort for analysis. Patients who underwent EBUS-TBNA procedure between September 2019 and August 2022 were included in this retrospective study. For cases diagnosed as benign and unclassified lesions by EBUS-TBNA, the final diagnosis was determined by further invasive surgery or a combination of therapy and clinical follow-up for at least 6 months. A total of 618 patients were included in the study, including 182 females (29.4%) and 436 males (70.6%). The mean age of all patients was 61.9 ± 10.5 years. These patients were successfully punctured by EBUS-TBNA to obtain pathological results. The pathological diagnosis results of EBUS-TBNA were compared with the final clinical diagnosis results as follows: 133 cases (21.5%) of benign lesions and 485 cases (78.5%) of malignant lesions were finally diagnosed. Among them, the pathological diagnosis was obtained by EBUS-TBNA in 546 patients (88.3%) (464 malignant lesions and 82 benign conditions), while EBUS-TBNA was unable to define diagnosis in 72 patients (11.6%). 20/72 non-diagnostic EBUS-TBNA were true negative. The overall diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of EBUS-TBNA were 91.3%, 100%, 100%, 27.8%, and 91.6% [95% confidence interval (CI): 89.1-93.6%], respectively. In this study, only one case had active bleeding without serious complications during the EBUS-TBNA procedure. Given its low invasiveness, high diagnostic accuracy, and safety, EBUS-TBNA is worth promoting in thoracic lesions.

Lung cancer detection based on computed tomography image using convolutional neural networks.

Lung cancer is the most common type of cancer, accounting for 12.8% of cancer cases worldwide. As initially non-specific symptoms occur, it is difficult to diagnose in the early stages. Image processing techniques developed using machine learning methods have played a crucial role in the development of decision support systems. This study aimed to classify benign and malignant lung lesions with a deep learning approach and convolutional neural networks (CNNs). The image dataset includes 4459 Computed tomography (CT) scans (benign, 2242; malignant, 2217). The research type was retrospective; the case-control analysis. A method based on GoogLeNet architecture, which is one of the deep learning approaches, was used to make maximum inference on images and minimize manual control. The dataset used to develop the CNNs model is included in the training (3567) and testing (892) datasets. The model's highest accuracy rate in the training phase was estimated as 0.98. According to accuracy, sensitivity, specificity, positive predictive value, and negative predictive values of testing data, the highest classification performance ratio was positive predictive value with 0.984. The deep learning methods are beneficial in the diagnosis and classification of lung cancer through computed tomography images.

Superoxide Dismutase Mimetic Avasopasem Manganese Enhances Radiation Therapy Effectiveness in Soft Tissue Sarcomas and Accelerates Wound Healing.

Soft tissue sarcomas (STSs) are mesenchymal malignant lesions that develop in soft tissues. Despite current treatments, including radiation therapy (RT) and surgery, STSs can be associated with poor patient outcomes and metastatic recurrences. Neoadjuvant radiation therapy (nRT), while effective, is often accompanied by severe postoperative wound healing complications due to damage to the surrounding normal tissues. Thus, there is a need to develop therapeutic approaches to reduce nRT toxicities. Avasopasem manganese (AVA) is a selective superoxide dismutase mimetic that protects against IR-induced oral mucositis and lung fibrosis. We tested the efficacy of AVA in enhancing RT in STSs and in promoting wound healing. Using colony formation assays and alkaline comet assays, we report that AVA selectively enhanced the STS (liposarcoma, fibrosarcoma, leiomyosarcoma, and MPNST) cellular response to radiation compared to normal dermal fibroblasts (NDFs). AVA is believed to selectively enhance radiation therapy by targeting differential hydrogen peroxide clearance in tumor cells compared to non-malignant cells. STS cells demonstrated increased catalase protein levels and activity compared to normal fibroblasts. Additionally, NDFs showed significantly higher levels of GPx1 activity compared to STSs. The depletion of glutathione using buthionine sulfoximine (BSO) sensitized the NDF cells to AVA, suggesting that GPx1 may, in part, facilitate the selective toxicity of AVA. Finally, AVA significantly accelerated wound closure in a murine model of wound healing post RT. Our data suggest that AVA may be a promising combination strategy for nRT therapy in STSs.

Short-term outcomes of laparoscopic and robotic limited resections of pancreatic neuroendocrine tumours of the uncinate process: Report of six cases and review of the literature.

Minimally invasive surgery (MIS) for limited resections for pancreatic uncinate lesions is not widely performed but can adequately treat benign or low-grade malignant lesions. The aim of this study was to evaluate the short-term outcomes of MIS-limited pancreatic resections for patients with suspected pancreatic neuroendocrine tumours (PNETs). This was a retrospective study of six consecutive patients who underwent MIS for PNET within a single institution between 2017 and 2022. Six patients underwent limited pancreas-preserving MIS of the uncinate process (uncinectomy or enucleation), of which two were performed through the robotic approach and four through laparoscopic approach. The median operation time was 212.5 (175-338.75) min, and the median blood loss was 50 (50-112.5) ml. The median post-operative hospital length of stay was 5.5 (3.75-11.5) days. Two patients (33.3%) had major post-operative morbidities (Clavien-Dindo ≥Grade 3). There were no open conversions or post-operative mortalities. Five patients had histologically proven Grade 1 neuroendocrine tumours. One was T2 and four were T1. This study suggests that limited MIS resections of pancreatic uncinate PNETs are a feasible procedure with good patient outcomes. It offers a safe alternative to radical surgical resections like pancreatoduodenectomies in selected patients with low-grade malignant or benign tumours.

Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study

PurposeTo investigate the feasibility of an artificial intelligence (AI)-based semi-automated segmentation for the extraction of ultrasound (US)-derived radiomics features in the characterization of focal breast lesions (FBLs).Material and methodsTwo expert radiologists classified according to US BI-RADS criteria 352 FBLs detected in 352 patients (237 at Center A and 115 at Center B). An AI-based semi-automated segmentation was used to build a machine learning (ML) model on the basis of B-mode US of 237 images (center A) and then validated on an external cohort of B-mode US images of 115 patients (Center B).ResultsA total of 202 of 352 (57.4%) FBLs were benign, and 150 of 352 (42.6%) were malignant. The AI-based semi-automated segmentation achieved a success rate of 95.7% for one reviewer and 96% for the other, without significant difference (p = 0.839). A total of 15 (4.3%) and 14 (4%) of 352 semi-automated segmentations were not accepted due to posterior acoustic shadowing at B-Mode US and 13 and 10 of them corresponded to malignant lesions, respectively. In the validation cohort, the characterization made by the expert radiologist yielded values of sensitivity, specificity, PPV and NPV of 0.933, 0.9, 0.857, 0.955, respectively. The ML model obtained values of sensitivity, specificity, PPV and NPV of 0.544, 0.6, 0.416, 0.628, respectively. The combined assessment of radiologists and ML model yielded values of sensitivity, specificity, PPV and NPV of 0.756, 0.928, 0.872, 0.855, respectively.ConclusionAI-based semi-automated segmentation is feasible, allowing an instantaneous and reproducible extraction of US-derived radiomics features of FBLs. The combination of radiomics and US BI-RADS classification led to a potential decrease of unnecessary biopsy but at the expense of a not negligible increase of potentially missed cancers.

Yield of MR-directed US for MRI-detected breast findings: how often can we avoid MR biopsy?

PurposeTo evaluate the yield of MR-directed ultrasound for MRI detected breast findings. MethodsThis retrospective study included 857 consecutive patients who had a breast MRI between January 2017–December 2020 and received a BI-RADS 4 assessment. Only exams recommended for MR-directed ultrasound were included in the study, yielding 765 patients. Findings were characterized by presence or absence of a sonographic correlate. Utilizing the electronic medical record, for those with a sonographic correlate, the size, location, and morphology were noted. Imaging guided (Ultrasound and MRI) pathology results as well as excisional pathology results were recorded. A multivariable logistical regression analysis was used to investigate the clinical utility of MR-directed ultrasound. ResultsThere were 1262 MRI-detected BI-RADS category 4 findings in 765 patients. Of the 1262 findings, MR-directed ultrasound was performed on 852 (68 %). Of these, 291/852 (34 %) had an ultrasound correlate, including 143/291 (49 %) benign lesions, 81/291 (28 %) malignant lesions, 16/291 (5 %) with high-risk pathology and 51/291 (18 %) unknown due to lost to follow-up. Of those findings with ultrasound correlates, 173/291 (59 %) represented masses, 69/291 (24 %) were regions of non-mass enhancement, 22/291 (7.6 %) were foci and 27/291 (9.3 %) fell into the category of other which included lymph node, cysts, and scar tissue. Masses were significantly more likely to be identified on MR-directed ultrasound (p < 0.0001) compared to foci. ConclusionThe yield of MR-directed ultrasound is significantly higher for masses, than foci and non-mass enhancement, which should be taken into consideration when recommending an MR-directed ultrasound.

Impact of Transfer Learning Using Local Data on Performance of a Deep Learning Model for Screening Mammography.

Purpose To investigate the issues of generalizability and replication of deep learning models by assessing performance of a screening mammography deep learning system developed at New York University (NYU) on a local Australian dataset. Materials and Methods In this retrospective study, all individuals with biopsy or surgical pathology-proven lesions and age-matched controls were identified from a South Australian public mammography screening program (January 2010 to December 2016). The primary outcome was deep learning system performance-measured with area under the receiver operating characteristic curve (AUC)-in classifying invasive breast cancer or ductal carcinoma in situ (n = 425) versus no malignancy (n = 490) or benign lesions (n = 44). The NYU system, including models without (NYU1) and with (NYU2) heatmaps, was tested in its original form, after training from scratch (without transfer learning), and after retraining with transfer learning. Results The local test set comprised 959 individuals (mean age, 62.5 years ± 8.5 [SD]; all female). The original AUCs for the NYU1 and NYU2 models were 0.83 (95% CI: 0.82, 0.84) and 0.89 (95% CI: 0.88, 0.89), respectively. When NYU1 and NYU2 were applied in their original form to the local test set, the AUCs were 0.76 (95% CI: 0.73, 0.79) and 0.84 (95% CI: 0.82, 0.87), respectively. After local training without transfer learning, the AUCs were 0.66 (95% CI: 0.62, 0.69) and 0.86 (95% CI: 0.84, 0.88). After retraining with transfer learning, the AUCs were 0.82 (95% CI: 0.80, 0.85) and 0.86 (95% CI: 0.84, 0.88). Conclusion A deep learning system developed using a U.S. dataset showed reduced performance when applied "out of the box" to an Australian dataset. Local retraining with transfer learning using available model weights improved model performance. Keywords: Screening Mammography, Convolutional Neural Network (CNN), Deep Learning Algorithms, Breast Cancer Supplemental material is available for this article. © RSNA, 2024 See also commentary by Cadrin-Chênevert in this issue.

Optimizing ultrafast dynamic contrast-enhanced MRI scan duration in the differentiation of benign and malignant breast lesions.

To determine the optimal scan duration for ultrafast DCE-MRI in effectively differentiating benign from malignant breast lesions. The study prospectively recruited participants who underwent breast ultrafast DCE-MRI from September 2021 to March 2023. A 30-phase breast ultrafast DCE-MRI on a 3.0-T MRI system was conducted with a 4.5-s temporal resolution. Scan durations ranged from 40.5 s to 135.0 s, during which the analysis is performed at three-phase intervals, forming eight dynamic sets (scan duration [SD]40.5s: 40.5 s, SD54s: 54.0 s, SD67.5s: 67.5 s, SD81s: 81.0 s, SD94.5s: 94.5 s, SD108s: 108.0 s, SD121.5s: 121.5 s, and SD135s: 135.0 s). Two ultrafast DCE-MRI parameters, maximum slope (MS) and initial area under the curve in 60 s (iAUC), were calculated for each dynamic set and compared between benign and malignant lesions. Areas under the receiver operating characteristic curve (AUCs) were used to assess their diagnostic performance. A total of 140 women (mean age, 47 ± 11 years) with 151 lesions were included. MS and iAUC from eight dynamic sets exhibited significant differences between benign and malignant lesions (all p < 0.05), except iAUC at SD40.5s. The AUC of MS (AUC = 0.804) and iAUC (AUC = 0.659) at SD67.5s were significantly higher than their values at SD40.5s (AUC = 0.606 and 0.516; corrected p < 0.05). No significant differences in AUCs for MS and iAUC were observed from SD67.5s to SD135s (all corrected p > 0.05). Ultrafast DCE-MRI with a 67.5-s scan duration appears optimal for effectively differentiating malignant from benign breast lesions. By evaluating scan durations (40.5-135 s) and analyzing two ultrafast DCE-MRI parameters, we found a scan duration of 67.5 s optimal for discriminating between these lesions and offering a balance between acquisition time and diagnostic efficacy. Ultrafast DCE-MRI can effectively differentiate malignant from benign breast lesions. A minimum of 67.5-sec ultrafast DCE-MRI scan duration is required to differentiate benign and malignant lesions. Extending the scan duration beyond 67.5 s did not significantly improve diagnostic accuracy.

Galectin-3 expression by thyroid neoplasms in a center in southwest Nigeria

Background: Galectin-3 (GAL-3), which is known to be expressed in certain normal tissues and is implicated in tumor progression and metastasis, serves as a valuable marker for distinguishing malignant thyroid lesions from benign thyroid lesions. This study aimed to assess the clinical and histopathological features of thyroid neoplasms at OAUTHC Ile Ife utilizing GAL-3 immunohistochemistry and to compare these findings with those of studies conducted in other regions of the world. Materials and Methods: Histology slides from 56 patients with thyroid neoplasms spanning a 20-year period were subjected to immunohistochemical staining with monoclonal antibodies against GAL-3. Evaluation was based on the 2022 World Health Organization Classification of Thyroid Neoplasms, categorizing marker expression as follows: 1+ (10–25% stained cells), 2+ (26–50% stained cells), 3+ (51–75% stained cells), and 4+ (&gt;75% stained cells). Negative staining indicated no staining, while localized staining (focal) represented &lt;50% of cells stained (1+ or 2+), and diffuse staining indicated &gt;50% of cells stained (3+ or 4+). Results: A total of 56 patients with neoplastic thyroid disease were studied, including 15 males and 41 females. Among the 16 benign neoplastic lesions, four (25.0%) exhibited focal GAL-3 expression, while 12 (75.0%) showed no expression. Among the forty malignant neoplastic lesions, five (12.5%) tested negative for the GAL-3 immunomarker, while 35 (87.5%) demonstrated positive expression to varying degrees. Among the positive malignant lesions, two (5.0%) displayed focal GAL-3 expression, while 33 (82.5%) exhibited diffuse staining of tumor cells. The diffuse and strong expression of GAL-3 was significantly greater in malignant thyroid neoplasms than in benign neoplasms (p&lt;0.05). The sensitivity and specificity of GAL-3 were 88% and 75%, respectively. Conclusion: Our findings align with the previous research indicating that diffuse and strong immunohistochemical expression of GAL-3 is indicative of thyroid gland malignancy. GAL-3 serves as a valuable tool in diagnosing thyroid neoplasms, particularly in cases where histomorphologic characteristics are inconclusive or unclear.

Identification of Chronic Pancreatitis Associated microRNAs and Genes for the Diagnosis of Pancreatic Cancer.

The timely identification of both malignant and nonmalignant pancreatic lesions has the potential to significantly enhance prognosis and implement risk management strategies across various levels. microRNAs (miRs) and their corresponding targets play a crucial role in the development of pancreatic lesions and can serve as valuable diagnostic and therapeutic targets. The objective of our study was to investigate potential diagnostic markers that can effectively differentiate between malignant and nonmalignant pancreatic lesions. Gene Expression Omnibus (GEO) database with GSE24279 dataset was utilized to screen differentially expressed miRNAs (DEMs). We utilized the TargetScanHuman database to predict the target genes associated with hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p. Furthermore, a cohort comprising healthy individuals (n = 52), chronic pancreatitis (CP; n = 34), and pancreatic adenocarcinoma (PAAD; n = 53) patients was recruited to ascertain the levels of plasma markers. We identified 3 miRNAs (hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p) and 2 proteins (PCDH1 and AMN) as potential diagnostic markers for distinguishing between CP and PAAD. The area under the curve (AUC) values for all markers exceeded .800. Notably, a combination of plasma PCDH1 and AMN demonstrated excellent diagnostic performance (AUC = .921; 95% CI: .866-.977; sensitivity = .792; specificity = .941) in discriminating between CP and PAAD. In addition, the model of hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p yielded an AUC of .928, sensitivity of .830, and specificity of .912, respectively. Plasma levels of miRNAs (hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p) and their corresponding targets (PCDH1 and AMN) hold promise as potential biomarkers for predicting PAAD in patients with CP.

Napsin A Immunoreactivity in "Hobnail" Epithelium in Benign Endometrial and Endocervical Polyps.

Endometrial and endocervical polyps not uncommonly exhibit focal benign "hobnail" change/metaplasia within the glandular epithelium, sometimes in association with inflammation or infarction. In most cases, this is readily recognized as benign but occasionally, especially in endometrial polyps, this change prompts consideration of a premalignant or malignant lesion, including early serous or clear cell carcinoma. Herein we highlight the previously unreported phenomenon of positive staining of this hobnail epithelium with Napsin A which has the potential to exacerbate concern for clear cell carcinoma. Endometrial (n = 22) and endocervical (n = 17) polyps showing hobnail change were stained with Napsin A. Six cases were positive (4 of 22 endometrial and 2 of 17 endocervical polyps). In all cases, Napsin A positivity was confined to the hobnail epithelium. The hobnail epithelium was positive with estrogen receptor and hepatocyte nuclear factor 1- beta and exhibited wild-type immunoreactivity with p53 in all cases where these markers were performed. In addition, in 2 of 3 uterine adenosarcomas with focal hobnail change the epithelium was Napsin A positive. Pathologists should be aware that Napsin A may be expressed in benign/reactive hobnail epithelium in endometrial and endocervical polyps and should not consider positivity with this marker as a diagnostic of clear cell carcinoma.