Detection Of Lesions Research Articles

3D endoanal ultrasound versus external phased array MRI in detection and evaluation of anal sphincteric lesions

BackgroundThe anal sphincteric complex is formed by internal and external sphincters making two partially overlapping tubes around the anal canal. Anal sphincteric lesions represent a spectrum of entities with different patients’ presentations and surgical managements. Endoanal ultrasound has an increasing role in detection and evaluation of anal sphincteric lesions as compared to MRI of the anal canal. The aim of this work was to compare between the 3D EAUA and external phased array MRI in detection and evaluation of anal sphincteric lesions.ResultsThere is almost perfect agreement of 97.92% (Κw = 0.972) between 3D EAUS and external phased array MRI in the detection of the internal anal sphincter lesions and fair agreement of 66.67% (Κw = 0.37) in the detection of the external anal sphincteric lesions.Conclusions3D EAUS and external phased array MRI are comparable imaging techniques in the detection of the internal anal sphincter lesions, while the MRI could detect more external sphincteric lesions than EAUS.

Artificial intelligence in detection of small bowel lesions and their bleeding risk: A new step forward

The present letter to the editor is related to the study with the title “Automatic detection of small bowel (SB) lesions with different bleeding risk based on deep learning models”. Capsule endoscopy (CE) is the main tool to assess SB diseases but it is a time-consuming procedure with a significant error rate. The development of artificial intelligence (AI) in CE could simplify physicians’ tasks. The novel deep learning model by Zhang et al seems to be able to identify various SB lesions and their bleeding risk, and it could pave the way to next perspective studies to better enhance the diagnostic support of AI in the detection of different types of SB lesions in clinical practice.

Deep Learning-Based Detect-Then-Track Pipeline for Treatment Outcome Assessments in Immunotherapy-Treated Liver Cancer.

Accurate treatment outcome assessment is crucial in clinical trials. However, due to the image-reading subjectivity, there exist discrepancies among different radiologists. The situation is common in liver cancer due to the complexity of abdominal scans and the heterogeneity of radiological imaging manifestations in liver subtypes. Therefore, we developed a deep learning-based detect-then-track pipeline that can automatically identify liver lesions from 3D CT scans then longitudinally track target lesions, thereby providing the evaluation of RECIST treatment outcomes in liver cancer. We constructed and validated the pipeline on 173 multi-national patients (344 venous-phase CT scans) consisting of a public dataset and two in-house cohorts of 28 centers. The proposed pipeline achieved a mean average precision of 0.806 and 0.726 of lesion detection on the validation and test sets. The model's diameter measurement reliability and consistency are significantly higher than that of clinicians (p = 1.6 × 10-4). The pipeline can make precise lesion tracking with accuracies of 85.7% and 90.8% then finally yield the RECIST accuracies of 82.1% and 81.4% on the validation and test sets. Our proposed pipeline can provide precise and convenient RECIST outcome assessments and has the potential to aid clinicians with more efficient therapeutic decisions.

Screening for pancreatic cancer in high-risk individuals using MRI: optimization of scan techniques to detect small lesions.

Pancreatic cancer has a dismal prognosis in the general population. However, early detection and treatment of disease in high-risk individuals can improve survival, as patients with localized disease and especially patients with lesions smaller than 10mm show greatly improved 5-year survival rates. To achieve early detection through MRI surveillance programs, optimization of imaging is required. Advances in MRI technologies in both hardware and software over the years have enabled reliable detection of pancreatic cancer at a small size and early stage. Standardization of dedicated imaging protocols for the pancreas are still lacking. In this review we discuss state of the art scan techniques, sequences, reduction of artifacts and imaging strategies that enable early detection of lesions. Furthermore, we present the imaging features of small pancreatic cancers from a large cohort of high-risk individuals. Refinement of MRI techniques, increased scan quality and the use of artificial intelligence may further improve early detection and the prognosis of pancreatic cancer in a screening setting.

Objective Comparison of White Light and Narrow-Band Imaging for Detecting Scars, Sulci and Nodules.

Narrow-band imaging (NBI) can improve detection of lesions in the aerodigestive tract. However, its role in benign lesions of the larynx is unclear. This study aims to determine whether NBI improves the detection of scars, sulci, and nodules compared to panchromatic lighting using objective image analysis. In total, 120 vocal folds (VFs) were analyzed with and without NBI (21 normal, 15 scars, 16 sulci, and 45 nodules). Each VF image had anterior, middle, and posterior thirds analyzed for brightness using an area morphometry software (Optimas 5.1a). The middle-third with the lesion was analyzed against surrounding VF segments for average and standard deviation (SD) in absolute grayscale. The use of panchromatic light resulted in greater illumination and grayscale values than NBI. All lesions tended to be in the mid-membranous fold. Under panchromatic light, change in brightness when comparing anterior versus middle (A-M) was +6.1% for normal, versus 6.5%, 8.1%, and 7.1% for sulci, nodules, and scars, respectively. Under NBI, they were 9.0% (normal), 12.3% (sulci), 13.7% (nodules), and 13.1% (scars). A greater SD of luminescence was observed at pathology sites (p < 0.05) when using NBI. The change in absolute grayscale at all lesion sites was greater when using NBI than when using panchromatic light (p < 0.05). NBI significantly enhanced the area of pathology in patients with nodules, sulci, and scars. Greater SD values in grayscale at pathologic sites were observed compared at normal sites. Thus, NBI may improve the detection of phonotraumatic lesions compared to panchromatic light. N/A Laryngoscope, 2024.

EyeXNet: Enhancing Abnormality Detection and Diagnosis via Eye-Tracking and X-ray Fusion

Integrating eye gaze data with chest X-ray images in deep learning (DL) has led to contradictory conclusions in the literature. Some authors assert that eye gaze data can enhance prediction accuracy, while others consider eye tracking irrelevant for predictive tasks. We argue that this disagreement lies in how researchers process eye-tracking data as most remain agnostic to the human component and apply the data directly to DL models without proper preprocessing. We present EyeXNet, a multimodal DL architecture that combines images and radiologists’ fixation masks to predict abnormality locations in chest X-rays. We focus on fixation maps during reporting moments as radiologists are more likely to focus on regions with abnormalities and provide more targeted regions to the predictive models. Our analysis compares radiologist fixations in both silent and reporting moments, revealing that more targeted and focused fixations occur during reporting. Our results show that integrating the fixation masks in a multimodal DL architecture outperformed the baseline model in five out of eight experiments regarding average Recall and six out of eight regarding average Precision. Incorporating fixation masks representing radiologists’ classification patterns in a multimodal DL architecture benefits lesion detection in chest X-ray (CXR) images, particularly when there is a strong correlation between fixation masks and generated proposal regions. This highlights the potential of leveraging fixation masks to enhance multimodal DL architectures for CXR image analysis. This work represents a first step towards human-centered DL, moving away from traditional data-driven and human-agnostic approaches.

Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies

Liver tumors, whether primary or metastatic, have emerged as a growing concern with substantial global health implications. Timely identification and characterization of liver tumors are pivotal factors in order to provide optimum treatment. Imaging is a crucial part of the detection of liver tumors; however, conventional imaging has shortcomings in the proper characterization of these tumors which leads to the need for tissue biopsy. Artificial intelligence (AI) and radiomics have recently emerged as investigational opportunities with the potential to enhance the detection and characterization of liver lesions. These advancements offer opportunities for better diagnostic accuracy, prognostication, and thereby improving patient care. In particular, these techniques have the potential to predict the histopathology, genotype, and immunophenotype of tumors based on imaging data, hence providing guidance for personalized treatment of such tumors. In this review, we outline the progression and potential of AI in the field of liver oncology imaging, specifically emphasizing manual radiomic techniques and deep learning-based representations. We discuss how these tools can aid in clinical decision-making challenges. These challenges encompass a broad range of tasks, from prognosticating patient outcomes, differentiating benign treatment-related factors and actual disease progression, recognizing uncommon response patterns, and even predicting the genetic and molecular characteristics of the tumors. Lastly, we discuss the pitfalls, technical limitations and future direction of these AI-based techniques.

Automated detection of steno-occlusive lesion on time-of-flight magnetic resonance angiography: an observer performance study.

Intracranial steno-occlusive lesions are responsible for acute ischemic stroke. However, the clinical benefits of artificial intelligence-based methods for detecting pathologic lesions in intracranial arteries have not been evaluated. We aimed to validate the clinical utility of an artificial intelligence model for detecting steno-occlusive lesions in the intracranial arteries. Overall, 138 TOF-MRA images were collected from two institutions, which served as internal (n = 62) and external (n = 76) test sets, respectively. Each study was reviewed by five radiologists (two neuroradiologists and three radiology residents) to compare the usage and non-usage of our proposed artificial intelligence model for TOF-MRA interpretation. They identified the steno-occlusive lesions and recorded their reading time. Observer performance was assessed using the area under the Jackknife free-response receiver operating characteristic curve and reading time for comparison. The average area under the Jackknife free-response receiver operating characteristic curve for the five radiologists demonstrated an improvement from 0.70 without artificial intelligence to 0.76 with artificial intelligence (P = .027). Notably, this improvement was most pronounced among the three radiology residents, whose performance metrics increased from 0.68 to 0.76 (P = .002). Despite an increased reading time upon using artificial intelligence, there was no significant change among the readings by radiology residents. Moreover, the use of artificial intelligence resulted in improved inter-observer agreement among the reviewers (the intraclass correlation coefficient increased from 0.734 to 0.752). Our proposed artificial intelligence model offers a supportive tool for radiologists, potentially enhancing the accuracy of detecting intracranial steno-occlusion lesions on TOF-MRA. Less-experienced readers may benefit the most from this model.ABBREVIATIONS: AI = Artificial intelligence; AUC = Area under the receiver operating characteristic curve; AUFROC = Area under the Jackknife free-response receiver operating characteristic curve; DL = Deep learning; ICC = Intraclass correlation coefficient; IRB = Institutional Review Boards; JAFROC = Jackknife free-response receiver operating characteristic.

The current infection with Helicobacter pylori and association with upper gastrointestinal lesions and risk of upper gastrointestinal cancer: Insights from multicenter population-based cohort study.

The relationship between Helicobacter pylori (H. pylori) infection and upper gastrointestinal (UGI) cancers is complex. This multicenter, population-based cohort study conducted in seven areas in China aimed to assess the correlation between current H. pylori infection and the severity of UGI lesions, as well as its association with the risk of gastric cancer (GC) and esophageal cancer (EC). From 2015 to 2017, 27,085 participants (aged 40-69) completed a standardized questionnaire, and underwent a 13C-urea breath test. Then a subset underwent UGI endoscopy to assess the UGI lesion detection rates. All individuals were followed up until December 2021 to calculate the hazard ratios (HRs) for UGI cancers. H. pylori infection prevalence was 45.9%, and among endoscopy participants, 22.2% had gastric lesions, 19.2% had esophageal lesions. Higher detection rates of gastric lesions were noted in the H. pylori-positive population across all lesion severity levels. Over a median follow-up of 6.3 years, 104 EC and 179 GC cases were observed, including 103 non-cardia gastric cancer (NCGC) cases and 76 cardia gastric cancer (CGC) cases. H. pylori-infected individuals exhibited a 1.78-fold increased risk of GC (HR 1.78, 95% confidence interval [CI] 1.32-2.40) but no significant increase in EC risk (HR 1.07, 95% CI 0.73-1.57). Notably, there was a higher risk for both NCGC and CGC in H. pylori-infected individuals. This population-based cohort study provides valuable evidence supporting the association between current H. pylori infection and the risk of both NCGC and CGC. These findings contribute to the empirical basis for risk stratification and recommendations for UGI cancer screening.

Accelerated Diffusion-Weighted Magnetic Resonance Imaging of the Liver at 1.5 T With Deep Learning-Based Image Reconstruction: Impact on Image Quality and Lesion Detection.

To perform image quality comparison between deep learning-based multiband diffusion-weighted sequence (DL-mb-DWI), accelerated multiband diffusion-weighted sequence (accelerated mb-DWI), and conventional multiband diffusion-weighted sequence (conventional mb-DWI) in patients undergoing clinical liver magnetic resonance imaging (MRI). Fifty consecutive patients who underwent clinical MRI of the liver at a 1.5-T scanner, between September 1, 2021, and January 31, 2022, were included in this study. Three radiologists independently reviewed images using a 5-point Likert scale for artifacts and image quality factors, in addition to assessing the presence of liver lesions and lesion conspicuity. DL-mb-DWI acquisition time was 65.0 ± 2.4 seconds, significantly (P < 0.001) shorter than conventional mb-DWI (147.5 ± 19.2 seconds) and accelerated mb-DWI (94.3 ± 1.8 seconds). DL-mb-DWI received significantly higher scores than conventional mb-DWI for conspicuity of the left lobe (P < 0.001), sharpness of intrahepatic vessel margin (P < 0.001), sharpness of the pancreatic contour (P < 0.001), in-plane motion artifact (P = 0.002), and overall image quality (P = 0.005) by reader 2. DL-mb-DWI received significantly higher scores for conspicuity of the left lobe (P = 0.006), sharpness of the pancreatic contour (P = 0.020), and in-plane motion artifact (P = 0.042) by reader 3. DL-mb-DWI received significantly higher scores for strength of fat suppression (P = 0.004) and sharpness of the pancreatic contour (P = 0.038) by reader 1. The remaining quality parameters did not reach statistical significance for reader 1. Novel diffusion-weighted MRI sequence with deep learning-based image reconstruction demonstrated significantly decreased acquisition times compared with conventional and accelerated mb-DWI sequences, while maintaining or improving image quality for routine abdominal MRI. DL-mb-DWI offers a potential alternative to conventional mb-DWI in routine clinical liver MRI.

PDSMNet: Parallel pyramid dual-stream modeling for automatic lung COVID-19 infection segmentations

Artificial intelligence-based segmentation models can assist the early-stage detection of lung COVID-19 infections or lesions from medical images with higher efficiency versus traditional techniques, yet challenges remain attributable to factors such as heterogeneities in infection traits, small infection regions, blurred boundary context, mixtures of varying infection regions, and obscure intensity contrast between lesions and normal tissues. This study aims to improve the performance of automatic segmentation of lung COVID-19 infections by proposing a novel approach named parallel pyramid dual-stream modeling network (PDSMNet) with two major contributions: (1) refined design of the framework including (a) a transformer module termed parallel pyramid dual-stream module (PDSM) to effectively preserve channel, spatial, and other latent features; (b) fusion of a multi-scale pyramid parallel-pooling module (MPM) that extracts features in parallel at differentiated scales; (c) calibration of the skip-connection architectures to optimize prediction and preserve features; (d) calibration of attention mechanisms reflecting multiple sources of information including parallel-, serial-, and cross-attention contexts; and (e) improvement of the integral functionality of the framework in curtailing the burdens of parameter computations in a multi-modality scenario. (2) loss functions accounting for the training losses of normal tissues, diseases, and boundaries respectively to enhance the performance of the network. The calibrated loss design allowing for a margin improves the capacity of predictions. We conducted experiments using three different datasets with different modalities and compared the proposed framework PDSMNet with two other benchmarks and eight similar state-of-the-art (SOTA) networks. The experiments observed consistent performance improvements across all datasets. PDSMNet attained asymptotically a maximum increase of 16.5%, 6.2%, and 15.5% for mean F1 score (mF1S), mean dice-score coefficient (mDSC), mean intersection over union (mIoU) versus SOTA PDEAtt-UNet, a maximum increase of 49.6%, 25.6%, and 38.0% for mF1S, mDSC, and mIoU versus InfNet, a maximum increase of 26.1%, 10.8%, 21.8% versus MiniSeg, a maximum increase of 14.0%, 3.0%, and 13.4% versus TransUNet, and a maximum increase of 37.9%, 16.8%, 31.2% versus Attention-UNet respectively. For other SOTA models such as MT-UNet, UCTransNet, and UTNetV2, a sizeable enhancement of performance was observed as well. PDSMNet also yielded asymptotically a maximum 30.6%, 14.8%, 26.3% increase of mF1S, mDSC, mIoU versus benchmark UNet, and a maximum increase of 42.8%, 21.5%, 33.3% versus benchmark UNet++ respectively. PDSMNet demonstrated reduced computational costs as well, yielding approximately 0.53 M of parameters and 6.55G of floating-point operations per second (FLOPs) respectively using different datasets, and the quantitative and qualitative ablation tests reinforced the effectiveness of the various components of the proposed framework.

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

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

Bioimpedance based biomarker for the detection of precancerous and cancerous lesions of the pancreas: feasibility animal study

BackgroundPancreatic cancer (PC) remains a significant healthcare challenge due to its aggressive nature and poor prognosis. The current gold standard that combines imaging modalities, endoscopy, and biopsies has limited diagnostic efficacy due to various shortcomings.MethodsWe propose a feasibility study for the use of a bioimpedance biomarker to detect PC. The biomarker was evaluated in a double blind study on ex vivo pancreata of mice: 15 LSL-KrasG12D; LSL-p53R172H; Pdx1-Cre, 2 LSL-KrasG12D, and 9 wild type controls (Study 1). To determine if the biomarker can distinguish between PC and acute pancreatitis (AP), we challenged it with 18 cerulein-induced AP and 6 saline-injected controls (Study 2).ResultsThe results from Study 1 showed 100% specificity and 94% sensitivity against histopathology outcomes; for Study 2 all AP and saline-injected pancreases were diagnosed as non-cancerous. Regression analysis revealed a positive correlation between biomarker and pathologically analyzed cancer-induced fibrosis (r(24)= 0.73 (p<0.001)).ConclusionThese findings demonstrate the potential of this bioimpedance biomarker as a diagnostic tool for PC.

Improved PAA algorithm for breast mass detection in mammograms

Mammography screening is instrumental in the early detection and diagnosis of breast cancer by identifying masses in mammograms. With the rapid development of deep learning, numerous deep learning-based object detection algorithms have been explored for mass detection studies. However, these methods often yield a high false positive rate per image (FPPI) while achieving a high true positive rate (TPR). To maintain a higher TPR while also ensuring lower FPPI, we improved the Probability Anchor Assignment (PAA) algorithm to enhance the detection capability for mammographic characteristics with our previous work. We considered three dimensions: the backbone network, feature fusion module, and dense detection heads. The final experiment showed the effectiveness of the proposed method, and the TPR/FPPI values of the final improved PAA algorithm were 0.96/0.56 on the INbreast datasets. Compared to other methods, our method stands distinguished with its effectiveness in addressing the imbalance between positive and negative classes in cases of single lesion detection.

3DFRINet: A Framework for the Detection and Diagnosis of Fracture Related Infection in Low Extremities Based on 18F-FDG PET/CT 3D Images

Fracture related infection (FRI) is one of the most devastating complications after fracture surgery in the lower extremities, which can lead to extremely high morbidity and medical costs. Therefore, early comprehensive evaluation and accurate diagnosis of patients are critical for appropriate treatment, prevention of complications, and good prognosis. 18Fluoro-deoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is one of the most commonly used medical imaging modalities for diagnosing FRI. With the development of deep learning, more neural networks have been proposed and become powerful computer-aided diagnosis tools in medical imaging. Therefore, a fully automated two-stage framework for FRI detection and diagnosis, 3DFRINet (Three Dimension FRI Network), is proposed for 18F-FDG PET/CT 3D imaging. The first stage can effectively extract and fuse the features of both modalities to accurately locate the lesion by the dual-branch design and attention module. The second stage reduces the dimensionality of the image by using the maximum intensity projection, which retains the effective features while reducing the computational effort and achieving excellent diagnostic performance. The diagnostic performance of lesions reached 91.55% accuracy, 0.9331 AUC, and 0.9250 F1 score. 3DFRINet has an advantage over six nuclear medicine experts in each classification metric. The statistical analysis shows that 3DFRINet is equivalent or superior to the primary nuclear medicine physicians and comparable to the senior nuclear medicine physicians. In conclusion, this study first proposed a method based on 18F-FDG PET/CT three-dimensional imaging for FRI location and diagnosis. This method shows superior lesion detection rate and diagnostic efficiency and therefore has good prospects for clinical application.

Abstract PO2-07-08: The efficacy of an MRI/US fusion technique for MRI-detected lesions previously undetected by conventional B-mode second-look US

Abstract Introduction Breast magnetic resonance imaging (MRI) is performed for preoperative screening of breast cancer and surveillance of BRCA gene carriers, and some lesions, called MRI-detected lesions, can only be detected by MRI. When MRI-detected lesions are found, second-look ultrasound (US) is recommended as the first choice for confirmation. However, the detection of MRI-detected lesions by conventional B-mode (cB-mode) second-look US varies from institution to institution, and it is often difficult to ensure objectivity and reproducibility. We use an MRI/US fusion technique called real-time virtual sonography (RVS) for the detection of MRI-detected lesions. RVS uses a magnetic position tracking system to display real-time US images on the same monitor as MRI images. Few reports on second-look US using RVS have examined its detection rate of MRI-detected lesions and the histopathological results of these lesions. In this study, we retrospectively reviewed MRI-detected lesions identified by second-look US using RVS but not by cB-mode second-look US from January 2018 to December 2022, with the goal of evaluating these lesions’ pathological characteristics. Materials and methods Consecutive patients who had one or more MRI-detected lesions not detected by cB-mode second-look US were enrolled in this study between January 2018 and December 2021. Second-look US using RVS was conducted after an additional supine MRI with a body surface coil was performed. Results A total of 36 patients with 38 lesions were included in this study. The mean age was 54 years old (range: 31–76). We were able to detect 31 (82%) of the 39 MRI-detected lesions by second-look US using RVS. These lesions were characterized as follows: mass, 14; non-mass-enhancement (NME), 13; and focal, 4. US-guided biopsy or excisional biopsy was performed on all lesions, and 11 (35%) of 31 lesions were malignant. We were able to detect 14 (74%) of the 19 mass lesions, and 3 were malignant (invasive ductal carcinoma (IDC) [luminal A type] in all 3 cases). We detected 13 (93%) of the 14 NME lesions, and 7 were malignant: 6 were ductal carcinoma in situ (DCIS) (high grade, 3; intermediate grade, 2; and low grade, 1), and 1 was IDC (luminal A type). Finally, we were able to detect 4 (80%) of the 5 focal lesions, 1 of which was malignant (DCIS [low grade]). Of 22 patients aged 55 years or younger, 3 had malignant lesions (IDC [luminal A type] in 2, DCIS [high grade] in 1). Of 15 patients aged 56 years or older, 8 had malignant lesions (DCIS in 7 [high grade, 2; intermediate grade, 2; low grade, 1] and IDC [luminal A type] in 1). Conclusions In this study, second-look US using RVS identified 82% of MRI-detected lesions that were not identified with cB-mode second-look US. Of these MRI-detected lesions, 35% were malignant. These results suggest that second-look US using RVS is useful for identifying MRI-detected lesions that are not detected by cB-mode second-look US. Citation Format: Masayuki Saito, Hirona Banno, Yukie Ito, Mirai Ido, Manami Goto, Takahito Ando, Yukako Mouri, Junko Kousaka, Kmihito Fujii, Tsuneo Imai, Shogo Nakano. The efficacy of an MRI/US fusion technique for MRI-detected lesions previously undetected by conventional B-mode second-look US [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-07-08.

Development and evaluation of RFID-integrated endoscopic clips for laparoscopic surgery marking.

As advancements in surgical instruments and techniques continue to evolve, minimally invasive surgery has become increasingly preferred as a means of reducing patient pain and recovery time. However, one major challenge in performing minimally invasive surgery for early gastrointestinal cancer is accurately identifying the location of the lesion. This is particularly difficult when the lesion is confined to the lumen of the intestine and cannot be visually confirmed from the outside during surgery. In such cases, surgeons must rely on CT or endoscopic imaging to locate the lesion. However, if the lesion is difficult to identify with these images or if the surgeon has less experience, it can be challenging to determine its precise location. This can result in an excessive resection margin, deviating from the goal of minimally invasive surgery. To address this challenge, researchers have been studying the development of a marker for identifying the lesion using a radio-frequency identification (RFID) system. One proposed method for clinical application of this detection system is to attach an RFID tag to an endoscopic hemostatic clip and fix it to the intended position, providing a stable marker for the inner wall of the organ. This approach has the potential to improve the accuracy and effectiveness of minimally invasive surgery for early gastrointestinal cancer. In the development of a marker for identifying gastrointestinal lesions using a radio-frequency identification (RFID) system, the shape of the clip and suitable materials for attaching the RFID tag were determined through finite element method (FEM) analysis. A prototype of the clip was then fabricated and ex-vivo experiments were conducted using porcine intestine to evaluate the stability of the clip in relation to its position. To further evaluate the performance of the RFID-integrated clip in vivo, the clip was placed in the gastric wall of the stomach of anesthetized porcine using an endoscopic instrument. The clip was then detected using a RFID detector designed for laparoscopic approach. And later, the accuracy of detection was confirmed by incising the lesion. The design and fabrication of a clip with varying thicknesses using STS316 and STS304 stainless steel were accomplished using the results of finite element method analysis. The stability of the clip was evaluated through ex-vivo experiments, showing it to be a viable option. In-vivo experiments were performed on anesthetized porcine, in which the RFID-integrated clip was placed in the gastric wall and detected using a custom-made RFID detector. The resection margin, measured at about 30 mm from the detector position, was accomplished with low error. These findings indicate the feasibility and efficacy of using an RFID-integrated clip as a marker in minimally invasive surgery for the identification of gastrointestinal lesions. The study evaluated the feasibility of using stainless steel clips for lesion detection in endoscopic surgery using computer-aided engineering analysis and ex-vivo experimentation. Results showed that STS304 was suitable for use while STS316L was not. The ex-vivo experiments revealed that the clip holding force and tissue retention length varied depending on the location of attachment. In-vivo experiments confirmed the accuracy and usefulness of the RFID lesion detection system. However, challenges remain for its use in clinical field, such as ensuring the stability of the clip and the safe attachment of the RFID tag, which requires further research for commercialization.

Abstract ED02-03: Innovative approaches at the diagnosis and treatment of invasive lobular carcinoma

Abstract Invasive Lobular Carcinoma (ILC) represents a unique subtype of breast cancer characterized by distinctive clinical and molecular features. This presentation aims to delve into innovative approaches in the diagnosis and treatment of ILC, focusing on novel techniques in imaging, unique molecular findings, signaling pathway alterations, targetable biomarkers, and microenvironment profiling that can pave the way for the development of novel therapies. Imaging Techniques in ILC: Accurate diagnosis is the foundation for effective treatment. ILC often presents diagnostic challenges due to the fact that lobular cells tends to infiltrate the affected organs in a diffuse manner instead of forming a discrete tumor mass making diagnostic imaging such as screening mammograms and CT scans less precise. This presentation will explore cutting-edge imaging techniques such as contrast-enhanced mammography, novel radioisotopes for positron emission tomography (PET) such as Fluoroestradiol F18 (FES), Fluciclovine and prostate-specific membrane antigen (PSMA) for improved detection and characterization of ILC lesions. Furthermore, we will discuss emerging modalities like radiomics and artificial intelligence, which hold promise in enhancing early diagnosis and risk assessment. Molecular Insights into ILC: ILC is distinguished by its unique molecular profile, notably the loss of E-cadherin expression. We will delve into recent advancements in understanding the molecular landscape of ILC, including genomic alterations, epigenetic changes, and the role of signaling pathways providing insights into potential therapeutic vulnerabilities. Targetable Biomarkers and Pathways: Identification of targetable biomarkers is essential for the development of precision therapies in ILC. This presentation will highlight promising targets, including ERBB2 (HER2), ROS1, TROP2, AKT1 along with other novel biomarkers. Microenvironment Profiling: The tumor microenvironment plays a pivotal role in cancer progression and therapy response. We will discuss recent advancements in characterizing the ILC microenvironment, including the immune cell infiltrate and stromal components. Understanding the dynamic interplay between the tumor and its microenvironment can inform the development of immunotherapies and combination strategies tailored to ILC. In conclusion, this presentation will provide a comprehensive overview of the latest innovations in ILC diagnosis and treatment. Our exploration of advanced imaging techniques, molecular findings, targetable pathways, biomarkers, and microenvironment profiling will shed light on the exciting potential for personalized therapeutic approaches in the management of ILC. By staying at the forefront of these developments, we can pave the way for improved outcomes and enhanced quality of life for patients with ILC. Citation Format: J. Mouabbi. Innovative approaches at the diagnosis and treatment of invasive lobular carcinoma [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr ED02-03.

Past, Present, and Future Diagnostic Methods for the Early Noninvasive Detection of Oral Premalignant Lesions: A State of the Art and Systematic Review.

Objectives: To provide an in-depth analysis of noninvasive methods for the early diagnosis of oral premalignant lesions, focusing on novel biomarkers and optical technologies, and to discuss their potential in improving the prognosis of patients with oral oncological diseases. Methods: This state-of-the-art review examines various noninvasive diagnostic techniques, including the utilization of salivary microRNAs and optical technologies such as Raman spectroscopy, elastic scattering spectroscopy, diffuse reflectance spectroscopy, narrow-band imaging, autofluorescence imaging, toluidine blue staining, and microendoscopy. Results: Several noninvasive techniques have shown varying degrees of effectiveness in detecting oral cancer. Autofluorescence imaging exhibited sensitivities up to 100% but had variable specificity. toluidine blue staining reported sensitivity between 77% and 100% for high-risk lesions or cancer, with specificity around 45% to 67%. Spectroscopy techniques achieved 72% to 100% sensitivities and specificities of 75% to 98%. Microendoscopy presented a sensitivity of 84% to 95% and a specificity of 91% to 95%. Conclusion: The review highlights the strengths and limitations of each noninvasive diagnostic method and their recent advancements. Although promising results have been demonstrated, there is a need for further development of reliable strategies for early detection and intervention in oral oncology.

Abstract PO5-19-11: A novel portable breast cancer screening method - initial institutional experience

Abstract Background: Breast ultrasound (US) is a remarkable supplementary tool alongside mammography and magnetic resonance imaging (MRI) for the detection of breast lesions. US imaging is a fast, reliable method to identify and localize breast pathology. Innovative technologies that aim to timely identification and prompt diagnosis of suspicious lesions in breast cancer screening remain a clinical need. ATUSA is a B-type rechargeable US, shaped as a wearable device. It uses a linear array of transducers with water acting as a coupling medium that recreates a 3-dimensional image of the scanned breast. The ATUSA portability makes this device a practical breast screening method during a healthcare office visit. We report the initial feasibility of ATUSA to detect breast lesions in an outpatient setting from a trial in progress. Methodology: Interim prospective data from 35 female patients presenting with a mass of equal or greater than 1 cm in size, with a baseline handheld US image and pathology reports that evidenced a cancer diagnosis or a lesion suspicious of malignancy. Breast cancer types included invasive ductal carcinoma (IDC), ductal carcinoma in situ (DCIS) and invasive lobular carcinoma (ILC), invasive mammary carcinoma (IMC) with mixed ductal and lobular features and atypical lobular hyperplasia (ALH). ATUSA and baseline handheld reference US images were cross-referenced from June 2022 to June 2023. Basic demographics, clinical characteristics, mammogram and/or MRI, and pathology reports were included in the analysis. Patients were followed up for a 30-day period to note occurrence of any adverse event (AE). Institutional Review Board (IRB) approved this study (protocol H-42616). Results: The 35 patients had a mean age of 57 years (range: 25 – 79), mean BMI 29.6 kg/m2 (range: 18.1 – 49.3). Mammographic breast density was fibroglandular tissue 15 (42.8%), followed by heterogeneously dense (HD) 13 (3.7%), mixed HD and fibroglandular 1 (2.8%), mildly dense 1 (2.8%), extremely dense 2 (5.7%), and extremely fatty 1 (2.8%). Breast sizes were 1 (2.8 %) A cup, 11 (31.4%) B cups, 1 (2.8%) B – C cups, 5 (14.2%) C cups, 1 (2.8%) C – D cups, 13 (37.1%) D cups, and 3 (8.5%) DD cups. The 35 scanned lesions had a diagnosis (compared with final resected pathology) classified as IDC 18 (51.4 %), DCIS 2 (5.7 %), mixed IDC and DCIS 6 (17.1 %), ILC 3 (8.5 %), IMC with mixed features 2 (5.7 %), IMC with DCIS 1 (2.8 %), ALH 1 (2.8 %), and 2 (5.7 %) benign masses. ATUSA successfully identified 17/35 (48.5 %) lesions from this group as malignant, from which 4 were hard to distinguish due to shadowing artifact and 3 were underestimated in size. A total of 15 (42.8 %) malignant lesions were missed by ATUSA. No AEs related to the ATUSA device were reported. Conclusion: The ATUSA provides the means to produce a good quality US image during a clinic visit or where a facility lacks conventional US imaging. When compared to the handheld US, ATUSA can be safely used in the outpatient setting without the need of trained specialists but requires further development and testing to match conventional breast imaging. Table 1. Basic clinical characteristics &amp; demographics Citation Format: Margarita Riojas-Barrett, Jessica Montalvan, Mary Bajomo, Ivan Marin, Quan Dang Nguyen, Karla A Sepulveda, Elizabeth Bonefas, Alastair Thompson, Stacey Carter. A novel portable breast cancer screening method - initial institutional experience [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-19-11.