Malignant Lesions Research Articles

Associated factors leading to misdiagnosis of a combined diagnostic model of different types of strain imaging and conventional ultrasound in evaluation of breast lesions: Selection strategy for using different types of strain imaging in evaluation of breast lesions

ObjectiveTo evaluate the effectiveness of a decision tree that integrates conventional ultrasound (CUS) with two different strain imaging (SI) techniques for diagnosing breast lesions, and to analyze the factors contributing to false negative (FN) and false positive (FP) in the decision tree’s outcomes. Materials and MethodsImaging and clinical data of 796 cases in the training set and 351 cases in the validation set were prospectively collected. A decision tree model that combines two types of SI and CUS was constructed, and its diagnostic performance was analyzed. Univariate analysis and multivariate analysis were applied to identify independent risk factors associated with FP and FN results of the decision tree model. ResultsSize, shape, margin, vascularity, the types of internal calcifications, EI score and VTI pattern were found to be significantly independently associated with the diagnosis of benign and malignant breast lesions. Therefore, size, shape, margin, vascularity, EI score and VTI pattern were used to construct decision tree models. The Tree (EI+VTI) model had the highest AUC. Both in the training and validation groups, the AUC of Tree (EI+VTI) was significantly higher compared with that of EI, VTI, and BI-RADS (all, P < 0.05). Orientation, posterior acoustic features and the types of internal calcifications were significantly positively associated with misdiagnosis results of Tree (EI+VTI) in evaluation of breast lesions (all P < 0.05). ConclusionThe diagnostic model based on a decision tree that integrates two distinct types of SI with CUS enhances the diagnostic accuracy of each method when used individually. This integration lowers the misdiagnosis rate, potentially assisting radiologists in more effective lesion assessments. When applying the decision tree model, attention should be paid to the orientation, posterior acoustic features, and the types of internal calcifications of the lesions.

Early diagnosis of pancreatic cancer: Shedding light on an unresolved challenge.

Diagnosing early-stage pancreatic cancer (PC) remains a clinical challenge. Hence, studying novel imaging aspects that could enhance the diagnostic accuracy of malignant pancreatic precursor lesions is imperative. This article aims to underscore the promising role of emerging imaging aspects that may facilitate the earlier diagnosis of PC, thereby improving its management and prognosis.

Evaluation of image-pro plus assisted superb microvascular imaging for differential diagnosis of renal masses

ObjectivePrevious research on diagnostic assessment by superb microvascular imaging (SMI) were based on qualitative or semi-quantitative assessments of vascularity, which may be subjective and unrepeatable by different sonographers. This study aimed to evaluate diagnostic performance of SMI Image-pro Plus (IPP) based vascular index (VI) for malignant renal masses. MethodWe retrospectively reviewed 222 masses in 214 patients who underwent SMI between August 2019 and August 2022 in our study. We evaluated the diagnostic performance of blood flow via Alder grade, VI based on both IPP and SMI. ResultsThe kappa consistency of the Adler grade and VI for renal masses was classified among different observers were 0.765 and 0.824. The intra-observers correlation ecoefficiency (ICC) were 0.727 and 0.874. Benign renal masses were mainly Adler grade 0, grade I, and grade II, VI was 4.30 ± 4.27 (Range 0.98–16.42); while malignant masses were mainly Adler grade III, VI was 14.95 ± 10.94 (Range 0.79–56.89). VI was higher in malignant than benign masses (t = 15.638, P < 0.01). Among the malignant masses, the mean VI in clear cell renal cell carcinoma was higher than that in papillary renal cell carcinoma and chromophobe renal cell carcinoma (F = 30.659, P < 0.01). The sensitivity, specificity and accuracy of SMI were 80.00%, 71.15%, and 78.64%, respectively. The sensitivity, specificity, and accuracy were 60.59%, 88.46%, and 80.18% by using a VI of 7.95 as the cutoff value to identify malignant lesions from benign masses yielded. VI had better diagnostic efficiency than ultrasonic characteristics and Adler grade in benign and malignant differential diagnosis (Z = 4.851, P < 0.01; Z = 2.732, P < 0.01). ConclusionVI was higher in malignant than benign in renal masses. In malignant masses, VI in CCRCC was higher than that in papillary renal cell carcinoma and ChRCC. As a noninvasive examination, it had important clinical significance in the differential diagnosis of renal masses. VI from IPP may assist sonographer in distinguish renal malignances as a quantitative tool for vascularity.

Pancreatic neuroendocrine tumours: a comparison of cytological classification systems

AimsCytological classification systems provide a standardised interpretation framework for reporting cytological specimens. Three well-known classification systems can be applied when reporting pancreatic cytology. This study aimed to compare the accuracy...

A meta-analysis of MRI radiomics-based diagnosis for BI-RADS 4 breast lesions

ObjectiveThe aim of this study is to conduct a systematic evaluation of the diagnostic efficacy of Breast Imaging Reporting and Data System (BI-RADS) 4 benign and malignant breast lesions using magnetic resonance imaging (MRI) radiomics.MethodsA systematic search identified relevant studies. Eligible studies were screened, assessed for quality, and analyzed for diagnostic accuracy. Subgroup and sensitivity analyses explored heterogeneity, while publication bias, clinical relevance and threshold effect were evaluated.ResultsThis study analyzed a total of 11 studies involving 1,915 lesions in 1,893 patients with BI-RADS 4 classification. The results showed that the combined sensitivity and specificity of MRI radiomics for diagnosing BI-RADS 4 lesions were 0.88 (95% CI 0.83–0.92) and 0.79 (95% CI 0.72–0.84). The positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were 4.2 (95% CI 3.1–5.7), 0.15 (95% CI: 0.10–0.22), and 29.0 (95% CI 15–55). The summary receiver operating characteristic (SROC) analysis yielded an area under the curve (AUC) of 0.90 (95% CI 0.87–0.92), indicating good diagnostic performance. The study found no significant threshold effect or publication bias, and heterogeneity among studies was attributed to various factors like feature selection algorithm, radiomics algorithms, etc. Overall, the results suggest that MRI radiomics has the potential to improve the diagnostic accuracy of BI-RADS 4 lesions and enhance patient outcomes.ConclusionMRI-based radiomics is highly effective in diagnosing BI-RADS 4 benign and malignant breast lesions, enabling improving patients’ medical outcomes and quality of life.

Comparison of the effectiveness of contrast-enhanced mammography in detecting malignant lesions in patients with extremely dense breasts compared to the all-densities population

PurposeTo assess the effectiveness of contrast-enhanced mammography (CEM) recombinant images in detecting malignant lesions in patients with extremely dense breasts compared to the all-densities population.Material and methods792 patients with 808 breast lesions, in whom the final decision on core-needle biopsy was made based on CEM, and who received the result of histopathological examination, were qualified for a single-centre, retrospective study. Patient electronic records and imaging examinations were reviewed to establish demographics, clinical and imaging findings, and histopathology results. The CEM images were reassessed and assigned to the appropriate American College of Radiology (ACR) density categories.ResultsExtremely dense breasts were present in 86 (10.9%) patients. Histopathological examination confirmed the presence of malignant lesions in 52.6% of cases in the entire group of patients and 43% in the group of extremely dense breasts. CEM incorrectly classified the lesion as false negative in 16/425 (3.8%) cases for the whole group, and in 1/37 (2.7%) cases for extremely dense breasts. The sensitivity of CEM for the group of all patients was 96.2%, speci­ficity – 60%, positive predictive values (PPV) – 72.8%, and negative predictive values (NPV) – 93.5%. In the group of patients with extremely dense breasts, the sensitivity of the method was 97.3%, specificity – 59.2%, PPV – 64.3%, and NPV – 96.7%.ConclusionsCEM is characterised by high sensitivity and NPV in detecting malignant lesions regardless of the type of breast density. In patients with extremely dense breasts, CEM could serve as a complementary or additional examination in the absence or low availability of MRI.

Ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features for automatic classification of ovarian masses according to O-RADS.

Accurate and rapid discrimination between benign and malignant ovarian masses is crucial for optimal patient management. This study aimed to establish an ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features to automatically classify the ovarian masses into low risk and intermediate-high risk of malignancy lesions according to the Ovarian- Adnexal Reporting and Data System (O-RADS). The ultrasound images of 1,080 patients with 1,080 ovarian masses were included. The training cohort consisting of 683 patients was collected at the South China Hospital of Shenzhen University, and the test cohort consisting of 397 patients was collected at the Shenzhen University General Hospital. The workflow included image segmentation, feature extraction, feature selection, and model construction. The pre-trained Resnet-101 model achieved the best performance. Among the different mono-modal features and fusion feature models, nomogram achieved the highest level of diagnostic performance (AUC: 0.930, accuracy: 84.9%, sensitivity: 93.5%, specificity: 81.7%, PPV: 65.4%, NPV: 97.1%, precision: 65.4%). The diagnostic indices of the nomogram were higher than those of junior radiologists, and the diagnostic indices of junior radiologists significantly improved with the assistance of the model. The calibration curves showed good agreement between the prediction of nomogram and actual classification of ovarian masses. The decision curve analysis showed that the nomogram was clinically useful. This model exhibited a satisfactory diagnostic performance compared to junior radiologists. It has the potential to improve the level of expertise of junior radiologists and provide a fast and effective method for ovarian cancer screening.

Value of peripheral blood rare cell EGFR gene amplification detection in the evaluation of benign and malignant pulmonary nodules

Objective: To explore the value of detection of epidermal growth factor receptor (EGFR) gene amplification in peripheral blood rare cells in the assessment of benign and malignant pulmonary nodules. Methods: A total of 262 patients with pulmonary nodules were selected as the retrospectively study subjects from the Second Affiliated Hospital of Army Military Medical University and Peking Union Medical College Hospital from July 2022 to August 2023. There were 98 males and 164 females, with the age range from 16 to 79 (52.1±12.1) years. The EGFR gene amplification testing was performed on the rare cells enriched from patients' peripheral blood, and the clinical manifestations, CT imaging features, histopathological and/or pathological cytological confirmed results of patients were collected. The receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value of the method of detection of EGFR gene amplification in peripheral blood rare cells, and its diagnostic efficacy was evaluated. Results: Among the 262 patients, 143 were malignant pulmonary nodules and 119 were benign pulmonary nodules. The differences between malignant pulmonary nodules and benign pulmonary nodules in nodule diameter and nodule density were statistically significant (both P<0.001), while the differences in age, gender and nodule number were not statistically significant (all P>0.05). The number [M (Q1, Q3)] of EGFR gene amplification positive rare cells in patients with malignant pulmonary nodule was 8 (6, 11), which was higher than that in patients with benign pulmonary nodule [2 (1, 4), P<0.001]. The ROC curve results showed that when the optimal cut-off value was 5 (that was, the number of EGFR gene amplification positive rare cells was>5), the area under the curve (AUC) of the detection of EGFR gene amplification in peripheral blood rare cells for discrimination of benign and malignant pulmonary lesions was 0.816 (95%CI: 0.761-0.870), with a sensitivity of 83.2%, a specificity of 80.7%, and an accuracy of 82.1%. Based on the analysis of the diameter of the nodules, the AUC for distinguishing between benign and malignant pulmonary nodules with diameter 5-9 mm and 10-30 mm was 0.797 (95%CI: 0.707-0.887) and 0.809 (95%CI: 0.669-0.949), respectively, with sensitivity, specificity and accuracy reached 75% or above. Based on the analysis of nodule density, the AUC for distinguishing between benign and malignant solid nodule and subsolid nodule was 0.845 (95%CI: 0.751-0.939) and 0.790 (95%CI: 0.701-0.880), respectively, with sensitivity, specificity and accuracy reached 75% or above. Based on the analysis of nodule number, the AUC for distinguishing between benign and malignant solitary pulmonary nodule and multiple pulmonary nodule was 0.830 (95%CI: 0.696-0.965) and 0.817 (95%CI: 0.758-0.877), respectively, with sensitivity, specificity and accuracy reached 80% or above. Conclusion: The detection of EGFR gene amplification in peripheral blood rare cells contributes to the evaluation of benign and malignant pulmonary nodules, and can be used in the auxiliary diagnosis of benign and malignant pulmonary nodules.

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.