AIMS:To assess whether contrast-enhanced ultrasound (CEUS)-derived intrahepatic transit times predict short-term hepatic decompensation in patients with compensated cirrhosis and to determine whether they provide incremental prognostic value over noninvasive markers. MATERIALS AND METHODS:In this prospective study, 46 patients with compensated cirrhosis underwent CEUS with time-intensity curve analysis to determine hepatic artery-to-hepatic vein (HA-HVTT) and portal vein-to-hepatic vein (PV-HVTT) transit times. Liver stiffness and clinical scores (MELD, MELD 3.0, Child-Pugh, ALBI, FIB-4) were recorded. Patients were followed for 180 days to assess decompensation (ascites, variceal bleeding, or encephalopathy). Diagnostic performance was evaluated using ROC analysis, and prognostic value was assessed using Cox regression. RESULTS:Twenty-three patients developed decompensation. PV-HVTT and HA-HVTT were significantly shorter in patients who decompensated (p<0.001). PV-HVTT showed excellent discrimination (AUC 0.928), outperforming liver stiffness and clinical scores (all p<0.01). A PV-HVTT cutoff of 2.38 seconds identified high-risk patients with a 6-month decompensation rate of 83.3%, compared with 13.6% in low-risk patients. PV-HVTT independently predicted decompensation (HR 0.37 per 0.5 s increase, p<0.001). HA-HVTT showed moderate performance. CONCLUSIONS:CEUS-derived transit times predict short-term decompensation in compensated cirrhosis and outperform established noninvasive markers. CEUS provides a functional assessment of intrahepatic hemodynamics and may improve risk stratification.

Aim: The aim of the study was to develop machine learning algorithms (MLA) for diagnosing acute graft dysfunction (AGD) in kidney transplant recipients based on contrast-enhanced ultrasound (CEUS) analysis of the graft.Materials and methods: This prospective study involved 71 patients with kidney transplant undergoing CEUS during follow-up. AGD wasdefined as an increase in serum creatinine levels of at least 25% compared to the baseline of the last three months. The control group consisted of patients with stable kidney graft function (SGF). The top five CEUS parameters that achieved the best discrimination between the AGD and SGF groups were selected based on ANOVA testing and then employed as input for training MLA (naïve Bayes (NB), k-nearest neighbors (k-NN), and logistic regression (LR)). The models were validated by leave-one-out cross-validation.Results: Among the 111 CEUS analyses, 21 corresponded to the AGD group and 90 to the SGF group. CEUS analyses yielded 44 parameters, from which five were selected: the wash out rate in segmental arteries,time to peak in segmental arteries, medullary mean transit time, renal mean transit time, and medullary time to fall. These five parameters were employed as input for MLA, yielding an AUROC of 0.68 for NB and k-NN and 0.72 for LR. The inclusion of graft survival in the MLA significantly improved discrimination accuracy, yielding an AUROC of 0.79 for NB, 0.76 for k-NN,and 0.81 for LR.Conclusions: The use of MLA represents a promising strategy for analyzing CEUS-derived parameters in the setting AGD.

The European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) has been publishing guidelines, position papers and also technical reviews. In addition, comments have been published for illustration of such papers including recently the role of ultrasound in cutaneous neoplasms but also in the hepatobiliary system, pancreas, lung and other organs. In the current paper, we aim to summarize the typical sonographic findings of the most commonly benign cutaneous lesions.

Cancer is one of the most severe diseases nowadays. Thus, tumor detection in a non-invasive and accurate manner is a challenging subject. Among these tumors, liver cancer is one of the most dangerous, being very common. Hepatocellular Carcinoma (HCC) is the most frequent malignant liver tumor. The golden standard for diagnosing HCC is mainly the biopsy, however invasive and risky, leading to infections, respectively to the spreading of the tumor through the body. We conceive computerized techniques for abdominal tumor recognition within medical images. Formerly, traditional, texture-based methods were involved for this purpose. Both classical texture analysis methods, as well as advanced, original texture analysis techniques, based on superior order statistics, were involved. The superior order Gray Level Cooccurrence Matrix (GLCM), as well as the Textural Microstructure Cooccurrence Matrices (TMCM) were employed and assessed. Recently, deep learning techniques based on Convolutional Neural Networks (CNN), their fusions with the conventional techniques, as well as their combinations among themselves, were assessed in the approached field. We present the most relevant aspects of this study in the current paper.

The Hepatocellular Carcinoma (HCC) represents the most frequent malignant liver tumor. It evolves from cirrhosis after a restructuring phase, at the end of which dysplastic nodules result, which can transform into HCC. The needle biopsy is the golden standard for HCC diagnosis, being, however, invasive, dangerous, as it could lead to infections, respectively to the spread of the tumor through the body. Ultrasonography is a medical examination method which is non-invasive, inexpensive, thus safe, and repeatable. In our research, we developed computerized, non-invasive methods for computer aided and automatic diagnosis of HCC, based on ultrasound images. In the current work, we explored the role of representative Convolutional Neural Networks (CNN), respectively of their combinations, to achieve an optimal classification accuracy. The considered CNNs were fused at classifier level, by employing various combination schemes, based on relevant feature selection, respectively on Kernel Principal Component Analysis (KPCA). At the end, a classification accuracy above 95

Cancer constitutes a major affection nowadays, leading to death in many situations. Pancreatic malignant tumors represent the fourth most frequent cause of cancer related death in United States and Europe. The most trustworthy cancer diagnosis method is the biopsy, but this technique raises several risks, the most relevant one being the danger of tumoral spread through the human body. Thus, in the context of our research, we employ computerized methods for achieving highly accurate abdominal tumor recognition and segmentation within medical images. In the current approach, we performed the automatic recognition of pancreatic malignant tumors within Computed Tomography (CT) images, with the aid of Convolutional Neural Networks (CNN), by employing last generation architectures, as well as their improved versions, respectively combinations of these structures at classifier and decision level. The classification performance was assessed through specific metrics, an accuracy above 98% being achieved.

The European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) has been publishing guidelines, position papers and also technical reviews. In addition, comments have been published for illustration of such papers including the hepatobiliary system, pancreas, lung and other organs. In the current paper, we aim to summarize the typical sonographic findings of the most commonly seen cutaneous neoplasms.

Summary Background and objectives The knowledge of depth infiltration in non‐melanoma skin cancer (NMSC) using pre‐operative ultrasound could enable clinicians to choose the most adequate therapeutic approach, avoiding unnecessary surgeries and expensive imaging methods, delaying diagnosis and treatment. Our single‐center retrospective study determined the usefulness of high‐frequency ultrasound (HFUS) for depth infiltration assessment in auricular and nasal NMSC and assessed the subsequent change in therapeutic approach. Patients and Methods In 60 NMSC cases, we assessed the accuracy of HFUS in cartilaginous/bone infiltration detection as well as the correlation of sonographic and histological parameters. Results In 16.6% of cases, a deep cartilaginous/bone involvement or locoregional disease was identified pre‐operatively, resulting in a changed therapeutical scheme of radio‐immunological treatment rather than surgery. In two cases, pre‐operative HFUS identified local cartilage infiltration, reducing the number of surgical procedures. Forty‐eight remaining lesions with no depth infiltration were excised; a correlation of > 99% between the histologic and sonographic tumor depth (p<0.001) was found. Conclusions Pre‐surgical HFUS influences the therapeutic management in NMSC by detecting subclinical involvement of deeper structures, avoiding more extensive diagnostics, reducing costs, and improving healthcare quality. High‐frequency ultrasound should be implemented in dermatosurgery before tumor excision for optimized therapy and improved patient counseling.

Introduction:Currently, magnetic resonance imaging (MRI) is the most commonly used imaging method in the assessment of the loco-regional extension in cervical cancer. Contrast-enhanced ultrasound (abbreviated CEUS) is being investigated as an alternative or complement to the MRI investigation. Objectives:To evaluate the performance of CEUS in identifying loco-regional invasion of cervical cancer compared to MRI, considered the accepted reference standard. Methods:Sixty-one patients with histopathologically confirmed cervical cancer were investigated as part of the pre-treatment workup by CEUS and MRI. We calculated the accuracy and concordance of CEUS versus MRI for tumor invasion in the vagina, bladder, rectum, parametrium, and uterus. For the time-intensity curve associated parameters analyzed (TTPK, AUC, peak intensity, wash in and wash out gradient) we calculated sensitivity, specificity and threshold value of positivity, for tumor invasion at the above-mentioned sites, with graphical representation of the ROC (receiver operating characteristic) curve. Results:CEUS was highly accurate in detecting bladder (93.4%, 95% CI: 87.2-99.6) and uterine invasion (88.5%, 95% CI: 80.5-96.5). Substantial agreement between CEUS and MRI was observed for invasion in the uterine body (k=0.77, 95% CI: 0.56-0.98) and bladder (k=0.56, 95% CI: 0.35-0.77). ROC curve analysis for loco-regional invasions showed that the wash in gradient at a cut-off value of 2.23 had a sensitivity of 76% and a specificity of 67% in predicting uterine invasion. Conclusions:Our results demonstrate high accuracy and good agreement between CEUS and MRI regarding especially uterine and bladder invasion. This imaging method could help select patients in early stages for fertility sparing surgery, and also be of use in cases in which early bladder invasion is suspected.

The Hepatocellular Carcinoma (HCC) is the most often met malignant tumor of the liver. It develops from cirrhosis, after a parenchyma restructuring phase, at the end of which dysplastic nodules result that can transform into HCC. Nowadays, HCC diagnosis is usually performed through needle biopsy, also through medical imaging, but most of the methods are invasive and/or expensive. Ultrasonography is the best option for screening, while computerized techniques must be developed as well to derive subtle information. In our previous research, we experimented with conventional techniques, based on advanced texture analysis methods and traditional classifiers, as well as with deep learning techniques, aiming to perform HCC recognition with maximum accuracy. We combined the CNN architectures with each other, respectively with the conventional techniques, at classifier level. In this work, we combined existing and original CNN architectures at decision level, we assessed the corresponding performance, and we compared it with our previous results.

Cancer is a severe affection nowadays, leading to death in many situations. Among the liver tumors, the most often met is Hepatocellular Carcinoma (HCC), being present for 75% of the primary liver cancer patients. Despite its’ invasive character, the most reliable cancer diagnosis method is biopsy. For performing both non-invasive and accurate disease assessment, computerized techniques are required. Tumor semantic segmentation is useful in this context, achieving tumor detection, localization, and extension estimation at the same time. In our current research, we developed and comparatively assessed high performance methods for segmenting liver tumor, based on Convolutional Neural Networks (CNN), as UNet, UNet++ and DeepLabV3+, involving two CT image datasets in our experiments. The assessment was performed by considering both the polar and cartesian image representations. At the end, a maximum DICE value of 80.92%, a maximum IoU value of 69.55%, respectively a maximum accuracy value of 99.82% resulted.

Hepatocellular Carcinoma (HCC) is the most frequent malignant liver tumor and the third cause of cancer-related deaths worldwide. For many years, the golden standard for HCC diagnosis has been the needle biopsy, which is invasive and carries risks. Computerized methods are due to achieve a noninvasive, accurate HCC detection process based on medical images. We developed image analysis and recognition methods to perform automatic and computer-aided diagnosis of HCC. Conventional approaches that combined advanced texture analysis, mainly based on Generalized Co-occurrence Matrices (GCM) with traditional classifiers, as well as deep learning approaches based on Convolutional Neural Networks (CNN) and Stacked Denoising Autoencoders (SAE), were involved in our research. The best accuracy of 91% was achieved for B-mode ultrasound images through CNN by our research group. In this work, we combined the classical approaches with CNN techniques, within B-mode ultrasound images. The combination was performed at the classifier level. The CNN features obtained at the output of various convolution layers were combined with powerful textural features, then supervised classifiers were employed. The experiments were conducted on two datasets, acquired with different ultrasound machines. The best performance, above 98%, overpassed our previous results, as well as representative state-of-the-art results.

Background and aims. To evaluate the performance of magnetic resonance imaging (MRI) in restaging locally advanced rectal cancers (LARC) after neoadjuvant chemoradiotherapy (nCRT), with pathologic correlation. Methods. 80 patients with LARC treated with neoadjuvant therapy, with restaging MRI and surgery, were enrolled and prospectively reviewed. The diagnostic accuracy of the restaging MRI was assessed for tumor (ymrT), nodal status (ymrN), circumferential resection margin (ymrCRM), extramural vascular invasion (ymrEMVI) and tumoral deposits (ymrN1c) by calculating the sensitivity (Se), specificity (Sp), negative predictive values (NPV) and positive predictive values (PPV). Response to treatment was classified as good response (complete/near complete) vs. poor response (poor/partial response). The agreement between the tumor regression grade at MRI (mrTRG) and pathology (pTRG) was reported, as well the performance of mrTRG to identify good responders. The correlation between restaging MRI and histopathology was assessed by Spearman correlation coefficient. Results. The MRI accuracy ranged between 63.8% and 92.5% for T stage and was 81.3% for N stage. All MRI parameters evaluated at restaging were statistically significant correlated with histopathology evaluation, but EMVI. There was moderate correlation for N and N1c and a positive strong correlation for T, CRM and TRG (Spearman correlation coefficient of 0.390 for mrN1c-pN1c, 0.428 for mrN-pN, 0.522 for mrCRM-pCRM, 0.550 for mrT-pT and 0.731 for mrTRG-pTRG). Diagnostic accuracy of anal sphincter invasion was 91.3%, with a negative predictive value (NPV) of 100%. Accuracy rate varied between 70% for partial response to 93.75% for complete response after nCRT. Conclusions. MR imaging had good accuracy in restaging LARCs after nCRT. Our results showed high MRI accuracy in detecting anal sphincter involvement for low rectal tumors, with high NPV to exclude tumoral invasion. Restaging MRI predicted well the tumor regression grade, with good diagnostic performance in differentiating good responders from poor/partial responders. The accuracy was high for detecting complete response.

tosum and Rothmund-Thomson syndrome. Only a few reports discuss the presence of lichenoid changes in DC; these early findings were attributed to “simultaneously occurring OLP.” White patches have been mislabeled as “leukoplakia” and are considered the main oral finding in DC. “Leukoplakia” is a descriptive clinical term which bears no diagnostic meaning and has interchangeable use in clinical medicine. It does not inform the nature of the lesion in study. The white keratoses seen in DC are scars, and their histopathology in our cases revealed only hyperkeratotic cicatricial mucosa. Patient 1 never developed leukokeratosis during a 5-year follow-up before dying of complications of bone marrow transplantation; his lichenoid lesions were followed by mucosal atrophy, and he showed in situ SCC on histopathology. The term “Marjolin ulcer” designates SCCs occurring at the site of scars and wounds such as in the sequelae of discoid lupus erythematosus, lupus vulgaris, epidermolysis bullosa, hidradenitis, and OLP. Oral SCC in DC should be added to this list, since this SCC clearly arises in the areas of chronic mucosal scarring, a factor that seems not directly related to the DC gene mutations. We postulate that persistent interface inflammation in DC promotes scarring of oral tissues. Atrophic or hyperkeratotic scars ultimately develop. These might lead to SCC. The genetically altered epithelium due to the underlying disease possibly contributes to earlier development of SCC compared to other scarring diseases such as OLP, as in patient 4 (16-year-old).

Renal cancer (RC) represents 3% of all cancers, with a 2% annual increase in incidence worldwide, opening the discussion about the need for screening. However, no established screening tool currently exists for RC. To tackle this issue, we assessed surface-enhanced Raman scattering (SERS) profiling of serum as a liquid biopsy strategy to detect renal cell carcinoma (RCC), the most prevalent histologic subtype of RC. Thus, serum samples were collected from 23 patients with RCC and 27 controls (CTRL) presenting with a benign urological pathology such as lithiasis or benign prostatic hypertrophy. SERS profiling of deproteinized serum yielded SERS band spectra attributed mainly to purine metabolites, which exhibited higher intensities in the RCC group, and Raman bands of carotenoids, which exhibited lower intensities in the RCC group. Principal component analysis (PCA) of the SERS spectra showed a tendency for the unsupervised clustering of the two groups. Next, three machine learning algorithms (random forest, kNN, naïve Bayes) were implemented as supervised classification algorithms for achieving discrimination between the RCC and CTRL groups, yielding an AUC of 0.78 for random forest, 0.78 for kNN, and 0.76 for naïve Bayes (average AUC 0.77 ± 0.01). The present study highlights the potential of SERS liquid biopsy as a diagnostic and screening strategy for RCC. Further studies involving large cohorts and other urologic malignancies as controls are needed to validate the proposed SERS approach.

Abstract Background Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine. Methods Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naïve Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types. Results Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 ± 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 ± 0.03) or SERS data (AUC = 0.84 ± 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 ± 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 ± 0.04) or SERS (AUC = 0.92 ± 0.05) individually, although SERS alone performed better in terms of classification accuracy. Conclusion miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged.

Purpose To evaluate MRI performance in restaging locally advanced rectal cancers (LARC) after neoadjuvant chemoradiotherapy (nCRT) and interobserver agreement in identifying complete response (CR) and near-complete response (nCR). Methods 40 patients with CR and nCR on restaging MRI, surgery and/or endoscopy were enrolled. Two radiologists independently scored the restaging MRI and reported the presence of split scar sign (SSS) and MRI tumor regression grade (mrTRG). Diagnostic accuracy and ROC curves were calculated for single and combined sequences, with inter-reader agreement. Results Diagnostic performance was good for detecting CR and weaker for nCR. T2WI had the highest AUCs among individual sequences. There was a significant positive correlation between SSS and CR, with high Sp (89.5%/73.7%) and PPV (90%/79.2%) for both Readers. Similar accuracy rates were observed for the combination of sequences, with AUCs of 0.828–0.847 for CR and 0.690–0.762 for nCR. Interobserver agreement was strong for SSS, moderate for T2WI, weak for the combination of sequences. Conclusions Restaging MRI had good diagnostic performance in identifying CR and nCR. SSS had high Sp and PPV in diagnosing CR, with a strong level of interobserver agreement. T2WI with DWI was the optimal combination of sequences for selecting good responders.

Objectives: The purpose of this study is to provide expert consensus recommendations to establish a global ultrasound curriculum for undergraduate medical students. Methods: 64 multi-disciplinary ultrasound experts from 16 countries, 50 multi-disciplinary ultrasound consultants, and 21 medical students and residents contributed to these recommendations. A modified Delphi consensus method was used that included a systematic literature search, evaluation of the quality of literature by the GRADE system, and the RAND appropriateness method for panel judgment and consensus decisions. The process included four in-person international discussion sessions and two rounds of online voting. Results: A total of 332 consensus conference statements in four curricular domains were considered: (1) curricular scope (4 statements), (2) curricular rationale (10 statements), (3) curricular characteristics (14 statements), and (4) curricular content (304 statements). Of these 332 statements, 145 were recommended, 126 were strongly recommended, and 61 were not recommended. Important aspects of an undergraduate ultrasound curriculum identified include curricular integration across the basic and clinical sciences and a competency and entrustable professional activity-based model. The curriculum should form the foundation of a life-long continuum of ultrasound education that prepares students for advanced training and patient care. In addition, the curriculum should complement and support the medical school curriculum as a whole with enhanced understanding of anatomy, physiology, pathophysiological processes and clinical practice without displacing other important undergraduate learning. The content of the curriculum should be appropriate for the medical student level of training, evidence and expert opinion based, and include ongoing collaborative research and development to ensure optimum educational value and patient care. Conclusions: The international consensus conference has provided the first comprehensive document of recommendations for a basic ultrasound curriculum. The document reflects the opinion of a diverse and representative group of international expert ultrasound practitioners, educators, and learners. These recommendations can standardize undergraduate medical student ultrasound education while serving as a basis for additional research in medical education and the application of ultrasound in clinical practice.

(1) Background: Romania has one of the highest cervical cancer incidence rates in Europe. In Cluj County, the first screening program was initiated in 1998. We aimed to investigate the time trends of cervical cancer incidence in women from Cluj County and to evaluate the data quality at the Cancer Registry. (2) Methods: We calculated time trends of standardized incidence rates in the period 1998–2014 and the Annual Percent Change (APC%). To assess data quality, we used the indicators: mortality/incidence ratio (M/I), percentage of cases declared only at death (DOD%), and percentage of cases with pathological confirmation (PC%). (3) Results: The standardized incidence rate increased steadily, from 23.74 cases/100,000 in 1998, to 32/100,000 in 2014, with an APC% of 2.49% (p < 0.05). The rise in incidence affected both squamous cell carcinoma (APC% 2.49%) (p < 0.05) and cervical adenocarcinoma (APC% 10.54%) (p < 0.05). The M/I ratio was 0.29, DOD% 2.66%, and MC% 94.8%. The last two parameters are within the silver standard concerning data quality. (4) Conclusions. Our study revealed an ascending trend of cervical cancer incidence, more consistent for adenocarcinoma, in the context of a newly introduced screening program and partially due to the improvement of the quality of case reporting at the Cancer Registry from Cluj.