Detection Of Lesions Research Articles

Low-contrast lesion detection in neck CT: a multireader study comparing deep learning, iterative, and filtered back projection reconstructions using realistic phantoms.

Computed tomography (CT) reconstruction algorithms can improve image quality, especially deep learning reconstruction (DLR). We compared DLR, iterative reconstruction (IR), and filtered back projection (FBP) for lesion detection in neck CT. Nine patient-mimicking neck phantoms were examined with a 320-slice scanner at six doses: 0.5, 1, 1.6, 2.1, 3.1, and 5.2 mGy. Each of eight phantoms contained one circular lesion (diameter 1 cm; contrast -30 HU to the background) in the parapharyngeal space; one phantom had no lesions. Reconstruction was made using FBP, IR, and DLR. Thirteen readers were tasked with identifying and localizing lesions in 32 images with a lesion and 20 without lesions for each dose and reconstruction algorithm. Receiver operating characteristic (ROC) and localization ROC (LROC) analysis were performed. DLR improved lesion detection with ROC area under the curve (AUC) 0.724 ± 0.023 (mean ± standard error of the mean) using DLR versus 0.696 ± 0.021 using IR (p = 0.037) and 0.671 ± 0.023 using FBP (p < 0.001). Likewise, DLR improved lesion localization, with LROC AUC 0.407 ± 0.039 versus 0.338 ± 0.041 using IR (p = 0.002) and 0.313 ± 0.044 using FBP (p < 0.001). Dose reduction to 0.5 mGy compromised lesion detection in FBP-reconstructed images compared to doses ≥ 2.1 mGy (p ≤ 0.024), while no effect was observed with DLR or IR (p ≥ 0.058). DLR improved the detectability of lesions in neck CT imaging. Dose reduction to 0.5 mGy maintained lesion detectability when denoising reconstruction was used. Deep learning enhances lesion detection in neck CT imaging compared to iterative reconstruction and filtered back projection, offering improved diagnostic performance and potential for x-ray dose reduction. Low-contrast lesion detectability was assessed in anatomically realistic neck CT phantoms. Deep learning reconstruction (DLR) outperformed filtered back projection and iterative reconstruction. Dose has little impact on lesion detectability against anatomical background structures.

Somatostatin receptors in fibrotic myocardium.

A patient with a neuroendocrine tumor and history of coronary artery disease underwent PET with 68Ga-DOTATATE PET tracer for tumor visualization. Analysis of the scan showed uptake of 68Ga-DOTATATE in the left ventricle corresponding to previous myocardial infarct. 68Ga-DOTATATE binds by somatostatin receptors (SSTR) and it has been proposed that it may be useful for the detection of cardiac inflammatory lesions. We aimed to test whether SSTR could be upregulated in cardiac fibrotic scar. We analyzed SSTR in cardiac samples from patients with end-stage ischemic cardiomyopathy (ICM, n = 8) and control hearts (n = 5). In mature ICM tissue, SSTR1 and SSTR2 expression was unchanged and SSTR5 expression was significantly decreased in ICM samples vs. control. Immunohistochemistry showed increased SSTR1 and SSTR2 in ICM. Areas with SSTR1 or SSTR2 staining were often adjacent to fibrotic areas. The majority of SSTR1 and SSTR2 staining localized in cardiomyocytes in fibrotic scar-rich areas where CD68 macrophage staining was not present. SSTR are occasionally upregulated in cardiac fibrotic areas. When using 68Ga-DOTATATE PET tracer to detect cardiac sarcoidosis or atherosclerotic plaque, the possibility of tracer uptake in fibrotic areas should be considered.

Deep Learning Algorithms for the Detection of Suspicious Pigmented Skin Lesions in Primary Care Settings: A Systematic Review and Meta-Analysis

Deep Learning Algorithms for the Detection of Suspicious Pigmented Skin Lesions in Primary Care Settings: A Systematic Review and Meta-Analysis

A systematic review of deep learning-based spinal bone lesion detection in medical images.

Spinal bone lesions encompass a wide array of pathologies, spanning from benign abnormalities to aggressive malignancies, such as diffusely localized metastases. Early detection and accurate differentiation of the underlying diseases is crucial for every patient's clinical treatment and outcome, with radiological imaging being a core element in the diagnostic pathway. Across numerous pathologies and imaging techniques, deep learning (DL) models are progressively considered a valuable resource in the clinical setting. This review describes not only the diagnostic performance of these models and the differing approaches in the field of spinal bone malignancy recognition, but also the lack of standardized methodology and reporting that we believe is currently hampering this newly founded area of research. In line with their established and reliable role in lesion detection, this publication focuses on both computed tomography and magnetic resonance imaging, as well as various derivative modalities (i.e. SPECT). After conducting a systematic literature search and subsequent analysis for applicability and quality using a modified QUADAS-2 scoring system, we confirmed that most of the 14 identified studies were plagued by major limitations, such as insufficient reporting of model statistics and data acquisition, a lacking external validation dataset, and potentially biased annotation. Although we experienced these limitations, we nonetheless conclude that the potential of these methods shines through in the presented results. These findings underline the need for more stringent quality controls in DL studies, as well as model development to afford increased insight and progress in this promising novel field.

Imaging modalities in neurolisteriosis: a literature review

Listeriosis caused by Listeria monocytogenes due to its ability to withstand harsh conditions and form biofilms on post-processed food poses a serious public health issue. It typically manifests itself with fever and gastrointestinal symptoms, but it can escalate with life-threatening conditions, especially in immunocompromised patients, the elderly, oncologically sick, and even pregnant women. The diagnosis is based on blood and cerebrospinal fluid culture growth, but it presents significant challenges due to deceptive findings and low positivity rates, the golden standard includes molecular diagnostic tests such as real-time PCR and metagenomic next-generation sequencing, which offer higher sensitivity and rapid detection. Radiological imaging, particularly magnetic resonance imaging, can play a crucial role in diagnosis of central nervous system (CNS) invasion by L. monocytogenes, enabling the detection of characteristic CNS lesions. The aim of the paper was to sum up the imaging features of L. monocytogenes CNS invasions in conventional imaging techniques, which can potentially speed up the diagnostic workflow for patients presenting with neurological symptoms associated with L. monocytogenes infection, particularly when conventional tests yield inconclusive results.

Descriptive evaluation of a camera-based dairy cattle lameness detection technology

Lameness in dairy cattle is a clinical sign of impaired locomotion, mainly caused by painful foot lesions, compromising the US dairy industry's economic, environmental, and social sustainability goals. Combining technology and on farm data may be a more precise and less labor-intensive lameness detection tool, particularly for early detection. The objective of this observational study was to describe the association between average weekly autonomous camera-based (AUTO) locomotion scores and hoof trimming (HT) data. The AUTO data were collected from 3 farms from April 2022 to March 2023. Historical farm HT data were collected from March 2016 to March 2023 and used to determine cow lesion history and date of HT event. The HT events were categorized as a regular HT (TRIM; n = 2290) or a HT with a lesion recorded (LESION; n = 670). Events with LESION were sub-categorized based on lesion category: digital dermatitis (DD; n = 276), sole ulcer (SU; n = 79), white line disease (WLD; n = 141), and other (n = 174). The data also contained the leg of the LESION, classified as front left (FL; n = 54), front right (FR; n = 146), rear left (RL; n = 281), or rear right (RR; n = 183) leg with 6 events missing the leg. Cows' HT histories were classified as follows: cows with no previous recorded instance of any lesion were classified as TRIM0 (n = 1554). The first instance of any hoof lesion was classified as LESION1 (n = 238). This classification was retained until a subsequent TRIM occurred - recorded as TRIM1 (n = 632). The next unique instance of any lesion following a TRIM1 was classified as LESION2 (n = 86). Any LESION events occurring after LESION1 or LESION2 without a subsequent TRIM were considered a hoof lesion recurrence and classified as LESIONRE1 (n = 164) and LESIONRE2 (n = 22), respectively. TRIM events after LESION2 or LESION2RE (n = 104) or LESION events after LESIONRE1 or LESIONRE2 were classified as LESION_OTHER (n = 160). The AUTO scores from -28 to -1 days prior to the HT event were summarized into weekly scores and included if cows had at least 1 observation per week in the 4 weeks before the event. For all weeks, LESION cows had a higher median AUTO score than TRIM cows. Cows with TRIM0 had the lowest and most consistent median weekly score compared to LESION and other TRIM classifications. Before HT cows with TRIM0 and TRIM1, both had median score increases of 1 across the 4 weeks, while the LESION categories had an increase of 4 to 8. Scores increased with each subsequent LESION event compared to the previous LESION event. Cows with SU lesions had the highest median score across the 4 weeks, WLD had the largest score increase, and DD had the lowest median score and score increase. When grouping a LESION event by leg the hoof lesion was found on, the AUTO scores for four groups displayed comparable median values. Due to the difference between TRIM and LESION events, this technology shows potential for the early detection of hoof lesions.

Exploring deep learning strategies for intervertebral disc herniation detection on veterinary MRI

Intervertebral Disc Herniation (IVDH) is a common spinal disease in dogs, significantly impacting their health, mobility, and overall well-being. This study initiates an effort to automate the detection and localization of IVDH lesions in veterinary MRI scans, utilizing advanced artificial intelligence (AI) methods. A comprehensive canine IVDH dataset, comprising T2-weighted sagittal MRI images from 213 pet dogs of various breeds, ages, and sizes, was compiled and utilized to train and test the IVDH detection models. The experimental results showed that traditional two-stage detection models reliably outperformed one-stage models, including the recent You Only Look Once X (YOLOX) detector. In terms of methodology, this study introduced a novel spinal localization module, successfully integrated into different object detection models to enhance IVDH detection, achieving an average precision (AP) of up to 75.32%. Additionally, transfer learning was explored to adapt the IVDH detection model for a smaller feline dataset. Overall, this study provides insights into advancing AI for veterinary care, identifying challenges and exploring potential strategies for future development in veterinary radiology.

Diagnostic ability of [18F]FDG PET/CT for distinguishing benign from malignant spleen lesions.

[18F]Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is a non-invasive imaging modality used in the differential diagnosis of splenic lesions, although ideal parameters and thresholds remain unclear. The present study evaluated the ability of [18F]FDG PET/CT, including its visual and quantitative parameters, to differentiate between benign and malignant splenic lesions. Patients who underwent [18F]FDG PET/CT following the detection of splenic lesions on contrast-enhanced CT were retrospectively analysed. Visual parameters assessed on [18F]FDG PET/CT included whole spleen uptake intensity, lesion multiplicity, and lesion uptake, and quantitative parameters included maximum standardised uptake value (SUVmax), lesion-to-background ratio (LBR), metabolic tumour volume (MTV), total lesion glycolysis (TLG), and lesion size. Parameters differentiating between benign and malignant lesions were evaluated by Pearson's chi-square test, Mann-Whitney U-test, and receiver operating characteristics (ROC) curve analysis. Splenic lesion uptake (p = 0.001) was the only visual parameter significantly distinguishing between benign and malignant lesions. ROC curve analysis demonstrated that SUVmax had the largest area under the ROC, 0.91 (p < 0.001), with an optimal cut-off > 5.3 having a sensitivity of 90.3% and a specificity of 80.6%. Subgroup analysis of malignant lesions showed that SUVmax (p = 0.013), LBR (p = 0.012), and TLG (p = 0.034) were significantly higher in splenic lymphomas than in splenic metastases. Of the [18F]FDG PET/CT parameters investigated, SUVmax had the highest accuracy in diagnosing malignant splenic lesions and was significantly higher in splenic lymphomas than in splenic metastases. Visual determination of [18F]FDG uptake by splenic lesions may be an easily evaluated parameter. SUVmax and visual grade of [18F]FDG PET/CT help to differentiate spleen lesions. [18F]FDG PET/CT is useful for discriminating between benign and malignant spleen lesions. Many splenic lesions are difficult to diagnose on anatomical imaging, with histopathologic analyses are required. SUVmax of PET/CT provided the diagnostic ability to differentiate between benign and malignant splenic lesions. More than normal spleen uptake can be a convenient parameter to diagnose malignant spleen lesions.

Detection of myeloma-associated osteolytic bone lesions with energy-integrating and photon-counting detector CT.

Arecent innovation in computed tomography (CT) imaging has been the introduction of photon-counting detector CT (PCD-CT) systems, which are able to register the number and the energy level of incoming x‑ray photons and have smaller detector elements compared with conventional CT scanners that operate with energy-integrating detectors (EID-CT). The study aimed to evaluate the potential benefits of anovel, non-CE certified PCD-CT in detecting myeloma-associated osteolytic bone lesions (OL) compared with astate-of-the-art EID-CT. Nine patients with multiple myeloma stageIII (according to Durie and Salmon) underwent magnetic resonance imaging (MRI), EID-CT, and PCD-CT of the lower lumbar spine and pelvis. The PCD-CT and EID-CT images of all myeloma lesions that were visible in clinical MRI scans were reviewed by three radiologists for corresponding OL. Additionally, the visualization of destructions to cancellous or cortical bone, and trabecular structures, was compared between PCD-CT and EID-CT. Readers detected 21% more OL in PCD-CT than in EID-CT images (138 vs. 109; p < 0.0001). The sensitivity advantage of PCD-CT in lesion detection increased with decreasing lesion size. The visualization quality of cancellous and cortical destructions as well as of trabecular structures was rated higher by all three readers in PCD-CT images (mean image quality improvements for PCD-CT over EID-CT were +0.45 for cancellous and +0.13 for cortical destructions). For myeloma-associated OL, PCD-CT demonstrated significantly higher sensitivity, especially with small size. Visualization of bone tissue and lesions was considered significantly better in PCD-CT than in EID-CT. This implies that PCD-CT scanners could potentially be used in the early detection of myeloma-associated bone lesions.

Cervical cancer screening by cytology and the burden of epithelial abnormalities in low resource settings: a tertiary-center 42-year study

BackgroundCytological screening remains a high-impact practice, particularly in low-resource settings, for preventing cervical cancer. The examination of screening practices over time and the prevalence of epithelial abnormalities have not been investigated in longitudinal studies in one of the largest countries in the Middle East and Africa.MethodsRoutine healthcare data, between March 1981 and December 2022, were extracted from the database of the Early Cancer Detection Unit in a tertiary referral university hospital in the Greater Cairo Region, Egypt. Cervical smears were obtained using a standardized technique and sent to the cytopathology laboratory for conventional cytology examination by expert pathologists. The anonymous data were analyzed to determine the temporal trend of the number of women screened each year and the prevalence of epithelial abnormalities.ResultsData included the results of satisfactory smears from 95120 women. The mean age (SD) of the women at the time of screening was 38.5 (10.5). None of the included women received an HPV vaccine. Abnormal epithelial cells were reported in 5174 women (5.44%). Of these epithelial abnormalities, the majority were low-grade squamous intraepithelial lesions in 4144 women (4.36%). Other abnormalities included atypical squamous cells in 378 women (0.40%), high-grade squamous intraepithelial lesions in 226 women (0.24%), atypical glandular cells not otherwise specified in 184 women (0.19%), adenocarcinoma in 165 women (0.17%), squamous cell carcinoma in 70 women (0.07%), and atypical glandular cells favoring neoplasms in 7 women (0.01%). Women who were at an early age at first intercourse, those who opted for routine cervical cytology screening, and those who were older at screening were more likely to have epithelial abnormalities. The yearly number of screened women was positively associated with the detection of low-grade squamous intraepithelial lesions (correlation coefficient [95% CI] = 0.84 [0.72, 0.91]) and negatively associated with the detection of squamous cell carcinoma (correlation coefficient [95% CI] = -0.55 [-0.73, -0.29]).ConclusionsThe small number of annually screened Egyptian women and the temporal trend in epithelial abnormalities critically demonstrate the need for establishing and scaling up a structured population-based program to achieve the goal of eliminating cervical cancer.

Reliability of impedance spectroscopy versus digital radiograph and ICDAS-II in occlusal caries detection: a prospective clinical trial

The traditional methods in early caries detection had many limitations. So, this study aimed to evaluate the clinical performance of alternating current impedance spectroscopy ACIST in comparison with digital radiograph and ICDAS-II in detection of occlusal carious lesions. Occlusal surfaces of molar and premolar teeth from 40 adult participants were examined by two observers using three diagnostic methods: (1) international caries detection and assessment system (ICDAS-II) (2) digital radiograph (DR) and (3) Cariescan Pro device (ACIST). Agreement analysis and the difference in sensitivities and specificities were evaluated. The results showed an excellent agreement in the different groups. The difference from the visual tactile against ACIST scoring for enamel caries detection, was statistically significant (p = 0.012) and the agreement was moderate (k = 0.509). For dentinal caries the difference was not statistically significant (p > 0.05) and the agreement was similarly moderate (k < 0.6). The difference from the digital radiograph against ACIST scoring, for enamel caries, digital radiography had significantly higher sensitivity and specificity than ACIST (p < 0.001) while for dentinal caries detection and overall, ACIST had higher sensitivity and digital radiography had higher specificity and the difference was statistically significant (p < 0.001). Visual-tactile examination is a considered as feasible and valid technique for occlusal caries detection, digital radiography is superior to ACIST in diagnosing enamel caries, but it could underestimate the caries depth, ACIST is a reliable tool for detecting occlusal caries in dentin.

Single-operator single-balloon enteroscopy is safe and effective: A 6-year retrospective study.

Single-balloon enteroscopy (SBE) is an effective tool for the detection of small intestine lesions. Because itis conventionally performed by two operators, the efficacy of single-operator SBE method has not yet been elucidated. We aimed to evaluate the diagnostic yield, total enteroscopy rate, procedure time, and complications of single-operator SBE for small intestinal disease. This was a single-center, retrospective study including consecutive patients who underwent single-operator SBE for suspicious small intestinal disorders or required therapeutic interventions between December 2014 and January 2019. The SBE procedures were performed by four endoscopists. Diagnostic yield, total enteroscopy rate, procedure time, incubation depth, and complications were analyzed, and stratification analysis was performed. Altogether 922 patients with 1422 SBE procedures were included for analysis, among whom 250, 172, and 500 patients underwent SBE via the oral route, the anal route and a combined route, respectively. The overall diagnostic yield was 78.52% (724/922). And 253 patients achieved total enteroscopy, with a total enteroscopy rate of 56.10%. The average procedure time for the oral and anal routes were 69.28 ± 14.72 min and 64.95 ± 13.87 min, respectively. While the incubation depth was 389.95 ± 131.42 cm and 191.81 ± 83.67 cm, respectively. Jejunal perforation was observed in one patient, which was managed by endoclips. Stratification analysis showed that the diagnostic yield and total enteroscopy rate significantly increased with operation experience together with decreased procedure time. Single-operator SBE is effective and safe for the detection of small intestinal lesions, and is easy to master.

Breast Cancer Detection and Localizing the Mass Area Using Deep Learning

Breast cancer presents a substantial health obstacle since it is the most widespread invasive cancer and the second most common cause of death in women. Prompt identification is essential for effective intervention, rendering breast cancer screening a critical component of healthcare. Although mammography is frequently employed for screening purposes, the manual diagnosis performed by pathologists can be laborious and susceptible to mistakes. Regrettably, the majority of research prioritizes mass classification over mass localization, resulting in an uneven distribution of attention. In response to this problem, we suggest a groundbreaking approach that seeks to identify and pinpoint cancers in breast mammography pictures. This will allow medical experts to identify tumors more quickly and with greater precision. This paper presents a complex deep convolutional neural network design that incorporates advanced deep learning techniques such as U-Net and YOLO. The objective is to enable automatic detection and localization of breast lesions in mammography pictures. To assess the effectiveness of our model, we carried out a thorough review that included a range of performance criteria. We specifically evaluated the accuracy, precision, recall, F1-score, ROC curve, and R-squared error using the publicly available MIAS dataset. Our model performed exceptionally well, with an accuracy rate of 93.0% and an AUC (area under the curve) of 98.6% for the detection job. Moreover, for the localization task, our model achieved a remarkably high R-squared value of 97%. These findings highlight that deep learning can boost the efficiency and accuracy of diagnosing breast cancer. The automation of breast lesion detection and classification offered by our proposed method bears substantial benefits. By alleviating the workload burden on pathologists, it facilitates expedited and accurate breast cancer screening processes. As a result, the proposed approach holds promise for improving healthcare outcomes and bolstering the overall effectiveness of breast cancer detection and diagnosis.

A deep learning anthropomorphic model observer for a detection task in PET.

Lesion detection is one of the most important clinical tasks in positron emission tomography (PET) for oncology. An anthropomorphic model observer (MO) designed to replicate human observers (HOs) in a detection task is an important tool for assessing task-based image quality. The channelized Hotelling observer (CHO) has been the most popular anthropomorphic MO. Recently, deep learning MOs (DLMOs), mostly based on convolutional neural networks (CNNs), have been investigated for various imaging modalities. However, there have been few studies on DLMOs for PET. The goal of the study is to investigate whether DLMOs can predict HOs better than conventional MOs such as CHO in a two-alternative forced-choice (2AFC) detection task using PET images with real anatomical variability. Two types of DLMOs were implemented: (1) CNN DLMO, and (2) CNN-SwinT DLMO that combines CNN and Swin Transformer (SwinT) encoders. Lesion-absent PET images were reconstructed from clinical data, and lesion-present images were reconstructed with adding simulated lesion sinogram data. Lesion-present and lesion-absent PET image pairs were labeled by eight HOs consisting of four radiologists and four image scientists in a 2AFC detection task. In total, 2268 pairs of lesion-present and lesion-absent images were used for training, 324 pairs for validation, and 324 pairs for test. CNN DLMO, CNN-SwinT DLMO, CHO with internal noise, and non-prewhitening matched filter (NPWMF) were compared in the same train-test paradigm. For comparison, six quantitative metrics including prediction accuracy, mean squared errors (MSEs) and correlation coefficients, which measure how well a MO predicts HOs, were calculated in a 9-fold cross-validation experiment. In terms of the accuracy and MSE metrics, CNN DLMO and CNN-SwinT DLMO showed better performance than CHO and NPWMF, and CNN-SwinT DLMO showed the best performance among the MOs evaluated. DLMO can predict HOs more accurately than conventional MOs such as CHO in PET lesion detection. Combining SwinT and CNN encoders can improve the DLMO prediction performance compared to using CNN only.

Cerebrospinal fluid cytology: A theragnostic and prognostic tool- Experience from a tertiary cancer care centre in central India

: CSF examination is the gold standard for diagnosing leptomeningeal metastases. Malignant cells may access the subarachnoid space by hematogenous dissemination, spreading directly from parenchymal brain lesions, or spreading along the spinal cord or cranial nerves. Cytological detection of malignant cells in CSF is still a crucial decision point for systemic and intrathecal chemotherapy, and it has prognostic significance even with improvements in biochemical analysis and CNS imaging. Over the course of two years, a total of 397 patients—regardless of gender and age—were investigated. There were sixty-four cases with involvement of the central nervous system documented, comprising 42 hematologic malignancies and 22 solid tumors. Cell centrifugation (Cytospin) method were used to prepare smears and cell blocks where prepared, as feasible and indicated. : A cytological analysis of the cerebrospinal fluid showed that metastatic tumors were much more prevalent than primary central nervous system tumours. Lung tumors were the most frequent primary site in our analysis, with breast, gastrointestinal, and female genital tract malignancies following closely behind. CNS involvement has been reported in patients with Lymphoma (DLBCL) and Leukemia (most commonly ALL, but also AML and JMML) among hematological malignancies. Two cases of Retinoblastoma and one each of Medulloblastoma and an Atypical teratoid/rhabdoid tumor were observed in the pediatric age group.: Cerebrospinal fluid cytology is a simple and useful method, used as a primary diagnostic method in the evaluation of leptomeningeal metastases. This helps in the early detection of secondary CNS lesions and allows for timely intervention, prognosis, and prediction of overall survival.

A graph-theoretic approach for the analysis of lesion changes and lesions detection review in longitudinal oncological imaging

Radiological follow-up of oncology patients requires the detection of lesions and the quantitative analysis of lesion changes in longitudinal imaging studies of patients, which is time-consuming and requires expertise.We present here a new method and workflow for the analysis and review of lesions and volumetric lesion changes in longitudinal scans of a patient. The generic graph-based method consists of lesion matching, classification of changes in individual lesions, and detection of patterns of lesion changes computed from the properties of the graph and its connected components. The workflow guides clinicians in the detection of missed lesions and wrongly identified lesions in manual and computed lesion annotations using the analysis of lesion changes. It serves as a heuristic method for the automatic revision of ground truth lesion annotations in longitudinal scans.The methods were evaluated on longitudinal studies of patients with three or more examinations of metastatic lesions in the lung (19 patients, 83 CT scans, 1178 lesions), the liver (18 patients, 77 CECT scans, 800 lesions) and the brain (30 patients, 102 T1W-Gad MRI scans, 317 lesions) with ground-truth lesion annotations. Lesion matching yielded a precision of 0.92–1.0 and recall of 0.91–0.99. The classification of changes in individual lesions yielded an accuracy of 0.87–0.97. The classification of patterns of lesion changes yielded an accuracy of 0.80–0.94. The lesion detection review workflow applied to manual and computed lesion annotations yielded 120 and 55 missed lesions and 20 and 164 wrongly identified lesions for all longitudinal studies of patients, respectively.The automatic analysis of lesion changes and review of lesion detection in longitudinal studies of oncological patients helps detect missed lesions and wrongly identified lesions. This method may help improve the accuracy of radiological interpretation and the disease status evaluation.

Efficacy of HPV E6/E7 mRNA assay, HPV DNA test and cytology in detection of high grade cervical lesions and invasive cancer at a tertiary care center in India

Efficacy of HPV E6/E7 mRNA assay, HPV DNA test and cytology in detection of high grade cervical lesions and invasive cancer at a tertiary care center in India

Biopsy of the facial nerve in slow-onset facial palsy.

Primary squamous cell carcinoma of the parotid gland typically presents as a palpable, often painless mass. Peripheral facial palsy as the only sign of malignant neoplasia is rare. In these cases, the diagnosis is regularly confirmed by radiological imaging followed by surgical exploration and biopsy. However, if there is no detection of malignant lesions and no evidence of a tumor, the reluctance to take a biopsy of an unremarkable nerve can lead to misdiagnoses. A 40-year-old female patient without medical history presented to our clinic with a complete right-sided peripheral facial palsy that had slowly progressed for 2.5 years. All other otorhinolaryngological examination findings were within normal limits. Magnetic resonance imaging examination of the head and neck and 18-fluorodeoxyglucose positron emission tomography showed unremarkable results. We proceeded with surgical exploration, which revealed no evidence of a tumor and an externally completely unremarkable facial nerve. A biopsy from the main trunk area of the nerve revealed an infiltration by a squamous cell carcinoma. Total parotidectomy with resection and reconstruction of the facial nerve and neck dissection was performed. Considering the absence of a primary tumor and other tumor formations the diagnosis of a completely regressive primary squamous cell carcinoma of the parotid gland was confirmed. In conclusion, in the case of slow-onset peripheral facial palsy that persists without signs of recovery, a gadolinium-enhanced MRI should be performed. If imaging is unremarkable and there is no primary tumor detection along the course of the facial nerve, a surgical exploration with biopsy of the facial nerve is necessary.

Asymmetric lesion detection with geometric patterns and CNN-SVM classification

In dermoscopic images, which allow visualization of surface skin structures not visible to the naked eye, lesion shape offers vital insights into skin diseases. In clinically practiced methods, asymmetric lesion shape is one of the criteria for diagnosing Melanoma. Initially, we labeled data for a non-annotated dataset with symmetrical information based on clinical assessments. Subsequently, we propose a supporting technique—a supervised learning image processing algorithm—to analyze the geometrical pattern of lesion shape, aiding non-experts in understanding the criteria of an asymmetric lesion. We then utilize a pre-trained convolutional neural network (CNN) to extract shape, color, and texture features from dermoscopic images for training a multiclass support vector machine (SVM) classifier, outperforming state-of-the-art methods from the literature. In the geometry-based experiment, we achieved a 99.00 % detection rate for dermatological asymmetric lesions. In the CNN-based experiment, the best performance is found 94 % Kappa Score, 95 % Macro F1-score, and 97 % weighted F1-score for classifying lesion shapes (Asymmetric, Half-Symmetric, and Symmetric).

Oral mucosal lesions triage via YOLOv7 models

Background/purposeThe global incidence of lip and oral cavity cancer continues to rise, necessitating improved early detection methods. This study leverages the capabilities of computer vision and deep learning to enhance the early detection and classification of oral mucosal lesions. MethodsA dataset initially consisting of 6903 white-light macroscopic images collected from 2006 to 2013 was expanded to over 50,000 images to train the YOLOv7 deep learning model. Lesions were categorized into three referral grades: benign (green), potentially malignant (yellow), and malignant (red), facilitating efficient triage. ResultsThe YOLOv7 models, particularly the YOLOv7-E6, demonstrated high precision and recall across all lesion categories. The YOLOv7-D6 model excelled at identifying malignant lesions with notable precision, recall, and F1 scores. Enhancements, including the integration of coordinate attention in the YOLOv7-D6-CA model, significantly improved the accuracy of lesion classification. ConclusionThe study underscores the robust comparison of various YOLOv7 model configurations in the classification to triage oral lesions. The overall results highlight the potential of deep learning models to contribute to the early detection of oral cancers, offering valuable tools for both clinical settings and remote screening applications.