Efficacy of fusion imaging for immediate post-ablation assessment of malignant liver neoplasms: A systematic review

Dual-energy computed tomography with new virtual monoenergetic image reconstruction enhances prostate lesion image quality and improves the diagnostic efficacy for prostate cancer

BACKGROUND

Prostate cancer is one of the most common malignant tumors in middle-aged and elderly men and carries significant prognostic implications, and recent studies suggest that dual-energy computed tomography (DECT) utilizing new virtual monoenergetic images can enhance cancer detection rates. This study aimed to assess the impact of virtual monoenergetic images reconstructed from DECT arterial phase scans on the image quality of prostate lesions and their diagnostic performance for prostate cancer.

METHODS

We conducted a retrospective analysis of 83 patients with prostate cancer or prostatic hyperplasia who underwent DECT scans at Meizhou People's Hospital between July 2019 and December 2023. The variables analyzed included age, tumor diameter and serum prostate-specific antigen (PSA) levels, among others. We also compared CT values, signal-to-noise ratio (SNR), subjective image quality ratings, and contrast-to-noise ratio (CNR) between virtual monoenergetic images (40-100 keV) and conventional linear blending images. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic efficacy of virtual monoenergetic images (40 keV and 50 keV) compared to conventional images.

RESULTS

Virtual monoenergetic images at 40 keV showed significantly higher CT values (168.19 ± 57.14) compared to conventional linear blending images (66.66 ± 15.5) for prostate cancer (P < 0.001). The 50 keV images also demonstrated elevated CT values (121.73 ± 39.21) compared to conventional images (P < 0.001). CNR values for the 40 keV (3.81 ± 2.13) and 50 keV (2.95 ± 1.50) groups were significantly higher than the conventional blending group (P < 0.001). Subjective evaluations indicated markedly better image quality scores for 40 keV (median score of 5) and 50 keV (median score of 5) images compared to conventional images (P < 0.05). ROC curve analysis revealed superior diagnostic accuracy for 40 keV (AUC: 0.910) and 50 keV (AUC: 0.910) images based on CT values compared to conventional images (AUC: 0.849).

CONCLUSIONS

Virtual monoenergetic images reconstructed at 40 keV and 50 keV from DECT arterial phase scans substantially enhance the image quality of prostate lesions and improve diagnostic efficacy for prostate cancer.

An effective no-reference image quality index prediction with a hybrid Artificial Intelligence approach for denoised MRI images

As the quantity and significance of digital pictures in the medical industry continue to increase, Image Quality Assessment (IQA) has recently become a prevalent subject in the research community. Due to the wide range of distortions that Magnetic Resonance Images (MRI) can experience and the wide variety of information they contain, No-Reference Image Quality Assessment (NR-IQA) has always been a challenging study issue. In an attempt to address this issue, a novel hybrid Artificial Intelligence (AI) is proposed to analyze NR-IQ in massive MRI data. First, the features from the denoised MRI images are extracted using the gray level run length matrix (GLRLM) and EfficientNet B7 algorithm. Next, the Multi-Objective Reptile Search Algorithm (MRSA) was proposed for optimal feature vector selection. Then, the Self-evolving Deep Belief Fuzzy Neural network (SDBFN) algorithm was proposed for the effective NR-IQ analysis. The implementation of this research is executed using MATLAB software. The simulation results are compared with the various conventional methods in terms of correlation coefficient (PLCC), Root Mean Square Error (RMSE), Spearman Rank Order Correlation Coefficient (SROCC) and Kendall Rank Order Correlation Coefficient (KROCC), and Mean Absolute Error (MAE). In addition, our proposed approach yielded a quality number approximately we achieved significant 20% improvement than existing methods, with the PLCC parameter showing a notable increase compared to current techniques. Moreover, the RMSE number decreased by 12% when compared to existing methods. Graphical representations indicated mean MAE values of 0.02 for MRI knee dataset, 0.09 for MRI brain dataset, and 0.098 for MRI breast dataset, showcasing significantly lower MAE values compared to the baseline models.

Retinex theory-based nonlinear luminance enhancement and denoising for low-light endoscopic images

BACKGROUND

The quality of low-light endoscopic images involves applications in medical disciplines such as physiology and anatomy for the identification and judgement of tissue structures. Due to the use of point light sources and the constraints of narrow physiological structures, medical endoscopic images display uneven brightness, low contrast, and a lack of texture information, presenting diagnostic challenges for physicians.

METHODS

In this paper, a nonlinear brightness enhancement and denoising network based on Retinex theory is designed to improve the brightness and details of low-light endoscopic images. The nonlinear luminance enhancement module uses higher-order curvilinear functions to improve overall brightness; the dual-attention denoising module captures detailed features of anatomical structures; and the color loss function mitigates color distortion.

RESULTS

Experimental results on the Endo4IE dataset demonstrate that the proposed method outperforms existing state-of-the-art methods in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). The PSNR is 27.2202, SSIM is 0.8342, and the LPIPS is 0.1492. It provides a method to enhance image quality in clinical diagnosis and treatment.

CONCLUSIONS

It offers an efficient method to enhance images captured by endoscopes and offers valuable insights into intricate human physiological structures, which can effectively assist clinical diagnosis and treatment.

Metal implant segmentation in CT images based on diffusion model

BACKGROUND

Computed tomography (CT) is widely in clinics and is affected by metal implants. Metal segmentation is crucial for metal artifact correction, and the common threshold method often fails to accurately segment metals.

PURPOSE

This study aims to segment metal implants in CT images using a diffusion model and further validate it with clinical artifact images and phantom images of known size.

METHODS

A retrospective study was conducted on 100 patients who received radiation therapy without metal artifacts, and simulated artifact data were generated using publicly available mask data. The study utilized 11,280 slices for training and verification, and 2,820 slices for testing. Metal mask segmentation was performed using DiffSeg, a diffusion model incorporating conditional dynamic coding and a global frequency parser (GFParser). Conditional dynamic coding fuses the current segmentation mask and prior images at multiple scales, while GFParser helps eliminate high-frequency noise in the mask. Clinical artifact images and phantom images are also used for model validation.

RESULTS

Compared with the ground truth, the accuracy of DiffSeg for metal segmentation of simulated data was 97.89% and that of DSC was 95.45%. The mask shape obtained by threshold segmentation covered the ground truth and DSCs were 82.92% and 84.19% for threshold segmentation based on 2500 HU and 3000 HU. Evaluation metrics and visualization results show that DiffSeg performs better than other classical deep learning networks, especially for clinical CT, artifact data, and phantom data.

CONCLUSION

DiffSeg efficiently and robustly segments metal masks in artifact data with conditional dynamic coding and GFParser. Future work will involve embedding the metal segmentation model in metal artifact reduction to improve the reduction effect.

Microwave ablation for colorectal liver metastases with ultrasound fusion imaging assistance: a stratified analysis study based on tumor size and location

PURPOSE

To investigate the efficacy of ultrasound fusion imaging-assisted microwave ablation (MWA) for patients with colorectal liver metastases (CRLM) based on stratified analysis of tumor size and location.

METHODS

Patients with CRLM who underwent ultrasound fusion imaging-assisted MWA in our hospital between February 2020 and February 2023 were enrolled into this retrospective study. Ultrasound fusion imaging was used for detection, guidance, monitoring and immediate evaluation throughout the MWA procedures. Technical success, technique efficacy, local tumor progression (LTP), intrahepatic progression and overall survival (OS) were recorded and analyzed. The subgroup analysis of intrahepatic progression of MWA for CRLM was performed according to tumor size and location.

RESULTS

A total of 51 patients with 122 nodules were enrolled. Both technical success and technique efficacy were acquired in all nodules. In a median follow-up period of 19 months, 2.5% of the nodules (3/122) were observed LTP. The 1-year and 2-year cumulative intrahepatic progression rates were 38.7% and 52.1% respectively. Patients were divided into subgroups according to tumor size (≥ 30 mm, n = 13; < 30 mm, n = 38) and tumor location (perivascular, n = 20; non-perivascular, n = 31 and subcapsular, n = 36; non-subcapsular, n = 15). The cumulative intrahepatic progression rates were similar between the subgroups regarding tumor size and perivascular location, while significantly higher in the subcapsular group than in the non-subcapsular group (p = 0.021).

CONCLUSION

Ultrasound fusion imaging-assisted MWA exhibited satisfactory local efficacy for CRLM, especially for non-subcapsular tumors.

Development of a flexible liver phantom for hepatocellular carcinoma treatment planning: a useful tool for training & education

PURPOSE

Hepatocellular carcinoma (HCC) is one of the most common types of liver cancer that could potentially be surrounded by healthy arteries or veins that a surgeon would have to avoid during treatment. A realistic 3D liver model is an unmet need for HCC preoperative planning.

METHODS

This paper presents a method to create a soft phantom model of the human liver with the help of a 3D-printed mold, silicone, ballistic gel, and a blender.

RESULTS

For silicone, the elastic modulus of seven different ratios of base silicone and silicone hardener are tested; while for ballistic gel, a model using 20% gelatin and 10% gelatin is created for the tumor and the rest of the liver, respectively. It is found that the silicone modulus of elasticity matches with the real liver modulus of elasticity. It is also found that the 10% gelatin part of the ballistic gel model is an excellent emulation of a healthy human liver.

CONCLUSION

The 3D flexible liver phantom made from a 10% gelatin-to-water mixture demonstrates decent fidelity to real liver tissue in terms of texture and elasticity. It holds significant potential for improving medical training, preoperative planning, and surgical research. We believe that continued development and validation of such models could further enhance their utility and impact in the field of hepatobiliary treatment planning and education.

STC-UNet: renal tumor segmentation based on enhanced feature extraction at different network levels

Renal tumors are one of the common diseases of urology, and precise segmentation of these tumors plays a crucial role in aiding physicians to improve diagnostic accuracy and treatment effectiveness. Nevertheless, inherent challenges associated with renal tumors, such as indistinct boundaries, morphological variations, and uncertainties in size and location, segmenting renal tumors accurately remains a significant challenge in the field of medical image segmentation. With the development of deep learning, substantial achievements have been made in the domain of medical image segmentation. However, existing models lack specificity in extracting features of renal tumors across different network hierarchies, which results in insufficient extraction of renal tumor features and subsequently affects the accuracy of renal tumor segmentation. To address this issue, we propose the Selective Kernel, Vision Transformer, and Coordinate Attention Enhanced U-Net (STC-UNet). This model aims to enhance feature extraction, adapting to the distinctive characteristics of renal tumors across various network levels. Specifically, the Selective Kernel modules are introduced in the shallow layers of the U-Net, where detailed features are more abundant. By selectively employing convolutional kernels of different scales, the model enhances its capability to extract detailed features of renal tumors across multiple scales. Subsequently, in the deeper layers of the network, where feature maps are smaller yet contain rich semantic information, the Vision Transformer modules are integrated in a non-patch manner. These assist the model in capturing long-range contextual information globally. Their non-patch implementation facilitates the capture of fine-grained features, thereby achieving collaborative enhancement of global-local information and ultimately strengthening the model's extraction of semantic features of renal tumors. Finally, in the decoder segment, the Coordinate Attention modules embedding positional information are proposed aiming to enhance the model's feature recovery and tumor region localization capabilities. Our model is validated on the KiTS19 dataset, and experimental results indicate that compared to the baseline model, STC-UNet shows improvements of 1.60%, 2.02%, 2.27%, 1.18%, 1.52%, and 1.35% in IoU, Dice, Accuracy, Precision, Recall, and F1-score, respectively. Furthermore, the experimental results demonstrate that the proposed STC-UNet method surpasses other advanced algorithms in both visual effectiveness and objective evaluation metrics.

Past, present, and future perspectives of ultrasound-guided ablation of liver tumors: Where could artificial intelligence lead interventional oncology?

The first ablation procedures for small hepatocellular carcinomas were percutaneous ethanol injection under ultrasound (US) guidance. Later, radiofrequency ablation was shown to achieve larger coagulation areas than percutaneous ethanol injection and became the most used ablation technique worldwide. In the past decade, microwave ablation systems have achieved larger ablation areas than radiofrequency ablation, suggesting that the 3-cm barrier could be broken in the treatment of liver tumors. Likewise, US techniques to guide percutaneous ablation have seen important progress. Contrast-enhanced US (CEUS) can define and target the tumor better than US and can assess the size of the ablation area after the procedure, which allows immediate retreatment of the residual tumor foci. Furthermore, fusion imaging fuses real-time US images with computed tomography or magnetic resonance imaging with significant improvements in detecting and targeting lesions with low conspicuity on CEUS. Recently, software powered by artificial intelligence has been developed to allow three-dimensional segmentation and reconstruction of the anatomical structures, aiding in procedure planning, assessing ablation completeness, and targeting the residual viable foci with greater precision than CEUS. Hopefully, this could lead to the ablation of tumors up to 5-7 cm in size.

Enhancing ECG-based heart age: impact of acquisition parameters and generalization strategies for varying signal morphologies and corruptions

Electrocardiogram (ECG) is a non-invasive approach to capture the overall electrical activity produced by the contraction and relaxation of the cardiac muscles. It has been established in the literature that the difference between ECG-derived age and chronological age represents a general measure of cardiovascular health. Elevated ECG-derived age strongly correlates with cardiovascular conditions (e.g., atherosclerotic cardiovascular disease). However, the neural networks for ECG age estimation are yet to be thoroughly evaluated from the perspective of ECG acquisition parameters. Additionally, deep learning systems for ECG analysis encounter challenges in generalizing across diverse ECG morphologies in various ethnic groups and are susceptible to errors with signals that exhibit random or systematic distortions To address these challenges, we perform a comprehensive empirical study to determine the threshold for the sampling rate and duration of ECG signals while considering their impact on the computational cost of the neural networks. To tackle the concern of ECG waveform variability in different populations, we evaluate the feasibility of utilizing pre-trained and fine-tuned networks to estimate ECG age in different ethnic groups. Additionally, we empirically demonstrate that finetuning is an environmentally sustainable way to train neural networks, and it significantly decreases the ECG instances required (by more than 100 × ) for attaining performance similar to the networks trained from random weight initialization on a complete dataset. Finally, we systematically evaluate augmentation schemes for ECG signals in the context of age estimation and introduce a random cropping scheme that provides best-in-class performance while using shorter-duration ECG signals. The results also show that random cropping enables the networks to perform well with systematic and random ECG signal corruptions.

Ensemble learning enhances the precision of preliminary detection of primary hepatocellular carcinoma based on serological and demographic indices

Primary hepatocellular carcinoma (PHC) is associated with high rates of morbidity and malignancy in China and throughout the world. In clinical practice, a combination of ultrasound and alpha-fetoprotein (AFP) measurement is frequently employed for initial screening. However, the accuracy of this approach often falls short of the desired standard. Consequently, this study aimed to investigate the enhancement of precision of preliminary detection of PHC by ensemble learning techniques. To achieve this, 712 patients with PHC and 1887 healthy controls were enrolled for the assessment of four ensemble learning methods, namely, Random Forest (RF), LightGBM, Xgboost, and Catboost. A total of eleven characteristics, comprising nine serological indices and two demographic indices, were selected from the participants for use in detecting PHC. The findings identified an optimal feature subset consisting of eight features, namely AFP, albumin (ALB), alanine aminotransferase (ALT), platelets (PLT), age, alkaline phosphatase (ALP), hemoglobin (Hb), and body mass index (BMI), that achieved the highest classification accuracy of 96.62%. This emphasizes the importance of the collective use of these features in PHC diagnosis. In conclusion, the results provide evidence that the integration of serological and demographic indices together with ensemble learning models, can contribute to the precision of preliminary diagnosis of PHC.

Preoperative single-dose camrelizumab and/or microwave ablation in women with early-stage breast cancer: A window-of-opportunity trial

BACKGROUND

Immune checkpoint blockade has shown low response rates for advanced breast cancer, and combination strategies are needed. Microwave ablation (MWA) may be a trigger of antitumor immunity. This window-of-opportunity trial (ClinicalTrials.gov: NCT04805736) was conducted to determine the safety and feasibility of preoperative camrelizumab (an anti-PD-1 antibody) combined with MWA in the treatment of early-stage breast cancer.

METHODS

Sixty participants were randomized to preoperatively receive single-dose camrelizumab alone (n = 20), MWA alone (n = 20), or camrelizumab+MWA (n = 20). A random number table was used to allocate interventions. The primary outcome was the safety and feasibility of MWA combined with camrelizumab.

FINDINGS

Camrelizumab and MWA were well tolerated alone and in combination without delays in prescheduled surgery. No treatment-related grade III/IV adverse events were observed. Different from in the single-dose camrelizumab or MWA group, participants showed stable counts of blood cells after combination therapy. After combination therapy, peripheral CD8+ T cells showed enhanced cytotoxic and effect-memory functions. Clonal expansional CD8+ T cells showed higher cytotoxic activity and effector memory- and tumor-specific signatures than emergent clones after combination therapy. Enhanced interactions between clonal expansional CD8+ T cells and monocytes were observed, suggesting that monocytes contributed to the enhanced functions of clonal expansional CD8+ T cells. Major histocompatibility complex (MHC) class I-related pathways and interferon signaling pathways were activated in monocytes by combination therapy.

CONCLUSIONS

Camrelizumab combined with MWA was feasible for early-stage breast cancer. Peripheral CD8+ T cells were activated after combination therapy, dependent on monocytes with activated MHC class I pathways.

FUNDING

This study was supported by the Natural Science Foundation of Jiangsu Province (BK20230017).

Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound

Elastography Ultrasound provides elasticity information of the tissues, which is crucial for understanding the density and texture, allowing for the diagnosis of different medical conditions such as fibrosis and cancer. In the current medical imaging scenario, elastograms for B-mode Ultrasound are restricted to well-equipped hospitals, making the modality unavailable for pocket ultrasound. To highlight the recent progress in elastogram synthesis, this article performs a critical review of generative adversarial network (GAN) methodology for elastogram generation from B-mode Ultrasound images. Along with a brief overview of cutting-edge medical image synthesis, the article highlights the contribution of the GAN framework in light of its impact and thoroughly analyzes the results to validate whether the existing challenges have been effectively addressed. Specifically, This article highlights that GANs can successfully generate accurate elastograms for deep-seated breast tumors (without having artifacts) and improve diagnostic effectiveness for pocket US. Furthermore, the results of the GAN framework are thoroughly analyzed by considering the quantitative metrics, visual evaluations, and cancer diagnostic accuracy. Finally, essential unaddressed challenges that lie at the intersection of elastography and GANs are presented, and a few future directions are shared for the elastogram synthesis research.

Estimating age and gender from electrocardiogram signals: A comprehensive review of the past decade

Twelve lead electrocardiogram signals capture unique fingerprints about the body's biological processes and electrical activity of heart muscles. Machine learning and deep learning-based models can learn the embedded patterns in the electrocardiogram to estimate complex metrics such as age and gender that depend on multiple aspects of human physiology. ECG estimated age with respect to the chronological age reflects the overall well-being of the cardiovascular system, with significant positive deviations indicating an aged cardiovascular system and a higher likelihood of cardiovascular mortality. Several conventional, machine learning, and deep learning-based methods have been proposed to estimate age from electronic health records, health surveys, and ECG data. This manuscript comprehensively reviews the methodologies proposed for ECG-based age and gender estimation over the last decade. Specifically, the review highlights that elevated ECG age is associated with atherosclerotic cardiovascular disease, abnormal peripheral endothelial dysfunction, and high mortality, among many other cardiovascular disorders. Furthermore, the survey presents overarching observations and insights across methods for age and gender estimation. This paper also presents several essential methodological improvements and clinical applications of ECG-estimated age and gender to encourage further improvements of the state-of-the-art methodologies.