Tumor Shape Research Articles

Estimation of the injection criteria for magnetic hyperthermia therapy based on tumor morphology

Intratumoral multi-injection strategy enhances the efficacy of magnetic nanoparticle hyperthermia therapy (MNPH). In this study, criteria for the selection of injections and their location depending on the tumor shape/geometry are developed. The developed strategy is based on the thermal dosimetry results of different invasive 3D tumor models during MNPH simulation. MNPH simulations are conducted on physical tumor tissue models encased within healthy tissue. The tumor shapes are geometrically divided into a central tumor region containing maximum tumor volume and a peripheral tumor portion protruding in any random direction. The concepts of core and invasive radius are used to geometrically divide the tumor volume. Primary & secondary injections are used to inject MNP fluid into these respective tumor regions based on the invasiveness of the tumor. The optimization strategy is devised based on the zone of influence of primary & secondary injection. Results indicate that the zone of influence of secondary injection lies between 0.7 and 0.8 times the radial distance between the center of the tumor core and branch node point (extreme far endpoint on the invasive tumor surface). Additionally, the multi-injection strategy is more effective when the protrusion volume exceeds 10% of the total volume. The proposed algorithm is used to devise multi-injection strategies for arbitrarily shaped tumors and will assist in pre-planning magnetic nanoparticle hyperthermia therapy.

Tumor morphology evaluation using 3D-morphometric features of renal masses.

To assess the correlation between the general (gender, age, and maximum tumor size) and 3D morphotopometric features of the renal tumor node, following the MSCT data post-processing, and the tumor histological structure; to propose an equation allowing for kidney malignancy assessment based on general and morphometric features. In total, 304 patients with unilateral solitary renal neoplasms underwent laparoscopic (retroperitoneoscopic) or robotic partial or radical nephrectomy. Before the procedure, kidney contrast-enhanced MSCT followed by the tumor 3D-modeling was performed. 3D model of the kidney tumor, and its morphotopometric features, and histological structure were analyzed. The morphotopometric ones include the side of the lesion, location by segments, the surface where the tumor, the depth of the tumor invasion into the kidney, and the shape of tumor. Out of 304 patients, 254 (83.6%) had malignant kidney tumors and 50 (16.4%) benign kidney tumors. In total, 231 patients, out of 254 (90.9%) were assessed for the degree of malignant tumor differentiation. Malignant tumors were more frequent in men than in women (p < 0.001). Mushroom-shaped tumors were the most common shapes among benign renal masses (35.2%). The most common malignant kidney tumors had spherical with a partially uneven surface (27.6%), multinodular (tuberous (27.2%)), and spherical with a conical base (24.8%) shapes. Logistic regression model enabled the development of prognostic equation for tumor malignancy prediction ("low" or "high"). The univariate analysis revealed the correlation only between high differentiation (G1) and a spherical tumor with a conical base (p = 0.029). The resulting logistic model, based on the analysis of such predictors as gender and form of kidney lesions, demonstrated a large share (87.6%) of correct predictions of the kidney tumor malignancy.

A Dynamic Context Encoder Network for Liver Tumor Segmentation.

Accurate segmentation of liver tumor regions in medical images is of great significance for clinical diagnosis and the planning of surgical treatments. Recent advancements in machine learning have shown that convolutional neural networks are powerful in such image processing while largely reducing human labor. However, the variable shape, fuzzy boundary, and discontinuous tumor region of liver tumors in medical images bring great challenges to accurate segmentation. The feature extraction capability of a neural network can be improved by expanding its architecture, but it inevitably demands more computing resources in training and hyperparameter tuning. This study presents a Dynamic Context Encoder Network (DCE-Net), which incorporates multiple new modules, such as the Involution Layer, Dynamic Residual Module, Context Extraction Module, and Channel Attention Gates, for feature extraction and enhancement. In the experiment, we used a liver tumor CT dataset of LiTS2017 to train and test the DCE-Net for liver tumor segmentation. The experimental results showed that the four evaluation indexes of the method, precision, recall, dice, and AUC, were 0.8961, 0.9711, 0.9270, and 0.9875, respectively. Furthermore, our ablation study reported that the accuracy and training efficiency of our network were markedly superior to the networks without involution or dynamic residual modules. Therefore, the DCE-Net proposed in this study has great potential for automatic segmentation of liver lesion tumors in the clinical diagnostic environment.

Predicting Choroidal Nevus Transformation to Melanoma Using Machine Learning

PurposeTo develop and validate machine learning (ML) models to predict choroidal nevus transformation to melanoma based on multimodal imaging at initial presentation. DesignRetrospective multicenter study. ParticipantsPatients diagnosed with choroidal nevus on the Ocular Oncology Service at Wills Eye Hospital (2007-2017) or Mayo Clinic Rochester (2015-2023). MethodsMultimodal imaging was obtained, including fundus photography, fundus autofluorescence, spectral domain OCT, and B-scan ultrasonography. ML models were created (XGBoost, LGBM, Random Forest, Extra Tree) and optimized for area under receiver operating curve (AUROC). The Wills Eye Hospital cohort was utilized for training and testing (80% training-20% testing) with 5-fold cross validation. The Mayo Clinic cohort provided external validation. Model performance was characterized by AUROC and area under the precision recall curve (AUPRC). Models were interrogated using SHapley Additive exPlanations (SHAP) to identify the features most predictive of conversion from nevus to melanoma. Differences in AUROC and AUPRC between models were tested using 10,000 bootstrap samples with replacement and results. Main Outcome MeasuresAUROC and AUPRC for each ML model. ResultsThere were 2,870 nevi included in the study, with conversion to melanoma confirmed in 128 cases. Simple AI Nevus Transformation System (SAINTS; XGBoost) was the top performing model in the test cohort [pooled AUROC 0.864 (95% confidence interval (CI): 0.864-0.865), pooled AUPRC 0.244 (0.243-0.246)] and in the external validation cohort [pooled AUROC 0.931 (95% CI: 0.930-0.931), pooled AUPRC 0.533 (0.531-0.535)]. Other models also had good discriminative performance: LGBM (test set pooled AUROC 0.831, validation set pooled AUROC 0.815), Random Forest (test set pooled AUROC 0.812, validation set pooled AUROC 0.866), and Extra Tree (test set pooled AUROC 0.826, validation set pooled AUROC 0.915). A model including only nevi with at least 5 years of follow up demonstrated the best performance in AUPRC (test: pooled 0.592 (95% CI: 0.590-0.594); validation: pooled 0.656 (95% CI: 0.655-0.657)). The top five features in SAINTS by SHAP values were: tumor thickness, largest tumor basal diameter, tumor shape, distance to optic nerve, and subretinal fluid extent. ConclusionsWe demonstrate accuracy and generalizability of a ML model for predicting choroidal nevus transformation to melanoma based on multimodal imaging.

Preoperative Ultrasonography Predicts Level II Lymph Node Metastasis in N1b Papillary Thyroid Carcinoma: Implications for Surgical Planning.

To investigate whether preoperative ultrasonographic (US) features of the index cancer and metastatic lymph nodes (LNs) are associated with level II LN metastasis in N1b papillary rmfthyroid carcinoma (PTC) patients. We enrolled 517 patients (mean age, 42 [range, 6-80] years) who underwent total thyroidectomy and lateral compartment LN dissection between January 2009 and December 2015. We reviewed the clinicopathologic and US features of the index cancer and metastatic LNs in the lateral neck. Logistic regression analysis was performed to analyze features associated with level II LN metastasis. Among the patients, 196 (37.9%) had level II metastasis on final pathology. In the preoperative model, larger tumor size (odds ratios [ORs], 1.031; 95% confidence interval [CI]: 1.011-1.051, p = 0.002), nonparallel tumor shape (OR, 1.963; 95% CI: 1.322-2.915, p = 0.001), multilevel LN involvement (OR, 1.906; 95% CI: 1.242-2.925, p = 0.003), and level III involvement (OR, 1.867; 95% CI: 1.223-2.850, p = 0.004), were independently associated with level II LN metastasis. In the postoperative model, non-conventional pathology remained a significant predictor for level II LN metastasis (OR, 1.951; 95% CI: 1.121-3.396; p = 0.018), alongside the presence of extrathyroidal extension (OR, 1.867; 95% CI: 1.060-3.331; p = 0.031), and higher LN ratio (OR, 1.057; 95% CI: 1.039-1.076; p < 0.001). Preoperative US features of the index tumor and LN may be helpful in guiding surgery in N1b PTC. These findings could enhance preoperative planning and decision-making, potentially reducing surgical morbidities by identifying those at higher risk of level II LN metastasis and tailoring surgical approaches accordingly.

BFT‐Net: A transformer‐based boundary feedback network for kidney tumour segmentation

AbstractKidney tumours are among the top ten most common tumours, the automatic segmentation of medical images can help locate tumour locations. However, the segmentation of kidney tumour images still faces several challenges: firstly, there is a lack of renal tumour endoscopic datasets and no segmentation techniques for renal tumour endoscopic images; secondly, the intra‐class inconsistency of tumours caused by variations in size, location, and shape of renal tumours; thirdly, difficulty in semantic fusion during decoding; and finally, the issue of boundary blurring in the localization of lesions. To address the aforementioned issues, a new dataset called Re‐TMRS is proposed, and for this dataset, the transformer‐based boundary feedback network for kidney tumour segmentation (BFT‐Net) is proposed. This network incorporates an adaptive context extract module (ACE) to emphasize local contextual information, reduces the semantic gap through the mixed feature capture module (MFC), and ultimately improves boundary extraction capability through end‐to‐end optimization learning in the boundary assist module (BA). Through numerous experiments, it is demonstrated that the proposed model exhibits excellent segmentation ability and generalization performance. The mDice and mIoU on the Re‐TMRS dataset reach 91.1% and 91.8%, respectively.

Enhancing brain tumor segmentation in MRI images using the IC-net algorithm framework

Brain tumors, often referred to as intracranial tumors, are abnormal tissue masses that arise from rapidly multiplying cells. During medical imaging, it is essential to separate brain tumors from healthy tissue. The goal of this paper is to improve the accuracy of separating tumorous regions from healthy tissues in medical imaging, specifically for brain tumors in MRI images which is difficult in the field of medical image analysis. In our research work, we propose IC-Net (Inverted-C), a novel semantic segmentation architecture that combines elements from various models to provide effective and precise results. The architecture includes Multi-Attention (MA) blocks, Feature Concatenation Networks (FCN), Attention-blocks which performs crucial tasks in improving brain tumor segmentation. MA-block aggregates multi-attention features to adapt to different tumor sizes and shapes. Attention-block is focusing on key regions, resulting in more effective segmentation in complex images. FCN-block captures diverse features, making the model more robust to various characteristics of brain tumor images. Our proposed architecture is used to accelerate the training process and also to address the challenges posed by the diverse nature of brain tumor images, ultimately leads to potentially improved segmentation performance. IC-Net significantly outperforms the typical U-Net architecture and other contemporary effective segmentation techniques. On the BraTS 2020 dataset, our IC-Net design obtained notable outcomes in Accuracy, Loss, Specificity, Sensitivity as 99.65, 0.0159, 99.44, 99.86 and DSC (core, whole, and enhancing tumors as 0.998717, 0.888930, 0.866183) respectively.

A comparative study of breast tumour detection using a semantic segmentation network coupled with different pretrained CNNs

ABSTRACT Breast cancer is one of the most prevalent malignancies and the primary origin of cancer-related deaths among females worldwide. Ultrasound image segmentation plays a crucial role in identifying breast tumours by precisely delineating the boundaries of the tumour within the images. Deep learning segmentation networks such as DeepLabV3+ have been used in the literature for this purpose. The coupling of DeepLabV3+ with base convolutional neural networks (CNN) can play a key role in its accuracy in tumour detection. The present study investigates the segmentation performance of four combinations of DeepLabV3+ segmentation network by coupling with the four base decoder CNN networks: DarkNet53, SqueezeNet, EfficientNet-b0 and DarkNet19. The accuracy of segmentation is confirmed using standard segmentation error metrics such as global and mean accuracy, mean and weighted Intersection over union (IoU), Boundary F1 (BF) Score and Dice Score. DeepLabV3+ coupled with DarkNet53 and EfficientNet-b0 as the decoder CNNs performed better than the other two combinations with a global accuracy of 96.50% and 96.18%. Precise tumour delineation can assist in tumour growth monitoring and treatment planning by providing detailed information on tumour size, shape, and location; thereby aiding in patient management and follow-up care.

Performance Evaluation of Ultrasound Images Using Non-Local Means Algorithm with Adaptive Isotropic Search Window for Improved Detection of Salivary Gland Diseases: A Pilot Study.

Speckle noise in ultrasound images (UIs) significantly reduces the accuracy of disease diagnosis. The aim of this study was to quantitatively evaluate its feasibility in salivary gland ultrasound imaging by modeling the adaptive non-local means (NLM) algorithm. UIs were obtained using an open-source device provided by SonoSkills and FUJIFILM Healthcare Europe. The adaptive NLM algorithm automates optimization by modeling the isotropic search window, eliminating the need for manual configuration in conventional NLM methods. The coefficient of variation (COV), contrast-to-noise ratio (CNR), and edge rise distance (ERD) were used as quantitative evaluation parameters. UIs of the salivary glands revealed evident visualization of the internal echo shape of the malignant tumor and calcification line using the adaptive NLM algorithm. Improved COV and CNR results (approximately 4.62 and 2.15 times, respectively) compared with noisy images were achieved. Additionally, when the adaptive NLM algorithm was applied to the UIs of patients with salivary gland sialolithiasis, the noisy images and ERD values were calculated almost similarly. In conclusion, this study demonstrated the applicability of the adaptive NLM algorithm in optimizing search window parameters for salivary gland UIs.

Ultrasound Evaluations of Major Salivary Gland Pathology

No single sonographic feature is enough to distinguish gland disorders. Major salivary gland diseases are either localized or diffuse. Imaging must identify neoplastic from non-neoplastic diseases using Ultrasound, which is a non-invasive, low-cost, and accessible imaging method for the major salivary gland evaluation. Color Doppler in addition to B-mode ultrasound assesses gland enlargement and detects inflammatory, benign, and malignant gland enlargement. Moreover, this study aims to assess the diagnostic effectiveness of ultrasonography in diagnosis of major salivary gland pathology. Method: Ninety Major Salivary Gland swelling patients underwent preoperative ultra-sonography with a high-frequency linear probe. Echotexture, internal Calcification, focal lesion borders, lymphadenopathy, tumor shape, duct dilatation, and blood supply distribution were examined using color Doppler and B-mode Ultrasound. Result: A histological investigation of 62 tumors identified 18 malignant and 44 benign, with 68.2% of the benign group being pleomorphic adenoma and 61.1% of the malignant group being mucoepidermoid carcinoma. The rest of the cases, 28 cases were inflammatory, 50% of which were sialolithiasis. The sonographic features of malignant and benign differ significantly in echogenicity, posterior echo-enhancement, vascularity, morphology, and homogeneity and Calcification. All groups show considerable differences in duct dilatation, duct calcification, and homogeneity. Conclusion: Ultrasonography demonstrating excellent diagnostic efficacy in assessing salivary gland function, based on these results Ultrasonography may help reduce unnecessary biopsies especially in inflammatory cases.

The Fluorescent Cell Line SW620-GFP Is a Valuable Model to Monitor Magnetic Hyperthermia.

In this work, the cell line SW620-GFP has been used in a complete magnetic hyperthermia assay, from the preparation of the ferrofluid with folate-coated iron oxide nanoparticles to in vivo experiments. The physical and chemical characterization of the nanoparticles evidenced their superparamagnetic behaviour, an average diameter of 12 ± 4 nm, a 2 nm coat thickness, and a high-power loss density. The main innovation of the work is the exclusive capability of viable SW620-GFP cells to emit fluorescence, enabling fast analysis of both, cell viability in vitro with an epifluorescence microscope and tumour size and shape in vivo in a non-invasive manner using the iBox technology. Moreover, with this imaging technique, it was possible to demonstrate the successful tumour size reduction in mice applying magnetic hyperthermia three times a week over 3 weeks.

IMG-23. AN MRI-BASED RADIOMIC SIGNATURE TO PREDICT LONG-TERM SURVIVAL IN PATIENTS WITH DIFFUSE INTRINSIC PONTINE GLIOMAS

Abstract BACKGROUND Diffuse intrinsic pontine gliomas are characterized by a short life expectancy after diagnosis, with a mean overall survival of less than 12 months. To stratify patients and possibly better adapt therapy, we are proposing a radiomic signature that predicts, at the time of diagnosis, which patients might survive beyond 2 years. METHODS A retrospective database comprising 80 patients who had undergone 3D multimodal MRI at the time of diagnosis was compiled. All images were acquired at a single center, and the protocol included T1-weighted images acquired before and after contrast injection, T2-weighted and FLAIR images. Depending on the patient, some modalities were missing (around 10% per modality). After manual tumor delineation, a dedicated radiomic pipeline extracted 79 features (first-order statistics and texture parameters) per imaging modality. In addition, gender, age and 14 tumor shape features were collected without missing data. After feature selection (maximum of 4 features per model), five logistic regression models were defined, one per modality and another for clinical and shape variables. Each patient was eligible for 2 to 5 models, with an average score calculated over these models. RESULTS Based on a leave-one-out approach, the multi-model was able to define patients who survive more than 2 years correctly, (sensitivity=100%, specificity =79%). A prediction could be made for each patient despite missing MR modalities. Defining a radiomic signature based on the multi-model score (cut-off=0.5), Kaplan-Meier analysis shows a good separation of the two groups (p-value&amp;lt;0.001). This result is superior to that obtained with the TP53 mutation (available for 61 patients, with p-value&amp;lt;0.01). CONCLUSIONS We have demonstrated the value of multimodal 3D MRI in predicting which DIPG patients might survive beyond 2 years. The robustness of this radiomic signature has yet to be tested using multicentric images.

Comparison of MRI features among squamous cell carcinoma, adenocarcinoma and adenosquamous carcinoma, usual-type endocervical adenocarcinoma and gastric adenocarcinoma of cervix

ObjectiveTo compare and explore the characteristics of squamous cell carcinoma (SCC), adenocarcinoma (AC) and adenosquamous carcinoma (ASC), usual-type endocervical adenocarcinoma (UEA) and gastric adenocarcinoma (GAC) of cervix. Materials and methodsA total of 728 cervical cancers (254 cases of AC, 252 cases of ASC, and 222 cases of SCC) confirmed by histopathology were retrospectively reviewed. Among AC, 119 UEA and 47 GAC were included. Clinical baseline data and tumor morphological features on MRI (including tumor location, shape, diameter and volume, margin, growth pattern, presence of fluid component or cyst, heterogenous and peritumoral enhancement) of all cases were collected and analyzed. The signal intensity (SI) of tumor and gluteus maximus muscle were measured and their ratios (SIR) were calculated based on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) and contrast-enhanced T1WI at arterial and delay phases (A/DCE-T1WI). These clinical and MRI features were compared between SCC, AC and ASC, UEA and GAC, and the specific ones of each subtype were identified. ResultsThere was a significant difference in SCC-Ag, CA-199, CEA, ADC value, SIR-DWI, presence of intratumor cyst and peritumoral enhancement between AC and ASC; in patient age, menopausal status, International Federation of Gynecology and Obstetrics (FIGO) stage, SCC-Ag, CA-125, CA-199, CEA, tumor shape, growth pattern, margin, presence of intratumor fluid component and cyst, tumor diameter and volume, ADC value, SIR-T1WI, SIR-T2WI, and SIR-DWI between SCC and AC, as well as SCC and ASC. Also, there was a significant difference in deep stromal invasion (DSI), peritumoral and heterogenous enhancement between SCC and AC, and in SIR-ACE-T1WI between SCC and ASC. There was a significant difference in reproductive history, menopausal status, FIGO stage, CA-199, DSI, lymph node metastasis (LNM), parametrial invasion (PMI), tumor location, shape, margin, growth pattern, presence of fluid component and cyst, tumor diameter and volume, SIR-T1WI, SIR-DWI, and heterogenous enhancement between GAC and UEA. ConclusionThe clinical and MRI features with significant differences among SCC, AC and ASC, and between UEA and GAC, can help to identify each subtype of cervical cancer.

Advancing precision medicine in cutaneous oncology through 3D high-frequency ultrasound imaging.

e13625 Background: Clinical assessment of tumor depth and shape often requires biopsy, especially when evaluating skin cancers. High Frequency Ultrasound (HFUS) is a non-invasive clinical tool that can quickly produce high resolution images ideally suited for evaluating topical tumors. In comparison to other imaging modalities, HFUS do not always provide the spatial data required to model 3-dimensional (3D) tumors. Currently, 3D-images are an amalgamation of many segmented image slices. However, in outpatient clinics it is unrealistic for HFUS devices and clinicians to gather large sets of image data. In our present original research, we aimed to overcome limited number of HFUS and dermatoscopic images by constructing a simple framework to process sparse and non-spatial data into working 3D skin tumor models. Methods: We analyzed two different skin tumors, the dermal duct tumor (DDT) and basal cell carcinoma (BCC), each with different number of HFUS images at 30Hz and 50Hz respectively. 1 set of 6 parallel images were obtained for the BCC, while 2 orthogonal sets of 6 parallel images were obtained for the DDT. For each tumor, a single dermatoscopic photo and sets of bitmap HFUS images with a resolution of 1024 x 512 were taken. Our study was IRB-approved. Mathematica and MATLAB, both with accessible documentation, were used to process images. Results: We developed a novel algorithmic approach to produce 3D tumor models with limited medical data. Both tumors appear corporally hypoechoic under HFUS. Mathematica was used to first reduce HFUS image noise and binarize the tumor region with a bilateral Gaussian filter. The user-friendly MATLAB Image Segmenter application manually filled the region of interest. Next, Mathematica was used to orient and plot tumor borders in 3D. Finally, we encompassed the borders with a 3D convex hull to conservatively predict tight margins around the tumor. Conclusions: Our simplified approach allows clinicians and scientists to produce 3D renderings of tumor margins. Margin control must always be balanced with a positive surgical outcome. By approaching our 3D-model with a convex hull around sampled border cross-sections, we chose a conservative approach that focuses skin tumor margin evaluation. Our results promote precision medicine and planning into the current standard of skin cancer treatment. Our findings can allow tracking tumor progression or treatment possible via non-invasive evaluation of tumor depth and contour. An aim of our present research is to lower the barrier of 3D construction in clinical settings. Simple algorithms, such as shown in our work, transform orthogonal and sparse data into usable data that can be implemented to train machine learning and artificial intelligent programs. Future research tailoring this approach may allow instant programming of HFUS images to non-invasively render cutaneous tumor borders and depth in real time during clinical examination.

GAIR-U-Net: 3D guided attention inception residual u-net for brain tumor segmentation using multimodal MRI images

Deep learning technologies have led to substantial breakthroughs in the field of biomedical image analysis. Accurate brain tumor segmentation is an essential aspect of treatment planning. Radiologists agree that manual segmentation is a difficult and time-consuming task that frequently causes delays in the diagnosing process. While U-Net-based methods have been widely used for brain tumor segmentation, many challenges persist, particularly when dealing with tumors of varying sizes, locations, and shapes. Additionally, segmenting tumor regions with structures requires a comprehensive model, which can increase computational complexity and potentially cause gradient vanishing issues. This study presents a novel method called 3D Guided Attention-based deep Inception Residual U-Net (GAIR-U-Net) to address these challenges. This model combines attention mechanisms, an inception module, and residual blocks with dilated convolution to enhance feature representation and spatial context understanding. The backbone of the model is the U-Net model, which leverages the power of inception and residual connections to capture intricate patterns and hierarchical features while expanding the model’s width in three-dimensional space without significantly increasing computational complexity. The attention mechanisms play a role in focusing on important regions and areas while downgrading irrelevant details. The dilated convolutions in the network help in learning both local and global information, improving accuracy and adaptability in segmenting tumors. All the experiments in this study were carried out on multimodal MRI scans that include (T1-weighted, T1-ce, T2-weighted, and FLAIR sequences) from the BraTS 2020 dataset. The presented model is trained and tested on the same dataset, which exhibited promising performance compared to previous methods. On the BraTS 2020 validation dataset, the proposed model obtained a dice score of 0.8796, 0.8634, and 0.8441 for whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively. These results demonstrate the model’s efficacy in precisely segmenting brain tumors across various modalities. Comparative analyses underscore the model’s versatility in handling tumor shape variations, size, and location, making it a promising solution for clinical applications.

Characteristics of the magnetic resonance imaging findings of cervical gastric-type adenocarcinoma

This study identified the distinct magnetic resonance imaging findings of cervical gastric-type adenocarcinoma (GAS) that can help differentiate it from squamous cell carcinoma (SCC) and usual-type endocervical adenocarcinoma (UEA) and reveal the radiologic-pathologic correlation. All consecutive patients with cervical GAS treated at our hospital from November 2009 to August 2021 were included. The SCC and UEA cases were considered controls. Tumor location, tumor shape, presence and size of cysts, presence of uterine fluid, and apparent diffusion coefficient (ADC) were evaluated. Overall, 18 GAS, 55 SCC, and 23 UEA cases were evaluated. The tumor was located in the entire cervix in 13/18 GAS cases, whereas it was predominantly located in the lower cervix in 38/55 SCC cases and 14/23 UEA cases. Most GAS cases exhibited a diffuse infiltration growth pattern (17/18), whereas most SCC and UEA cases exhibited a mass-forming pattern (39/55 and 20/23, respectively). Moreover, the percentages of cases presenting microcysts or macrocysts and undergoing uterine fluid collection were significantly higher in the GAS group (14/18 and 13/18) than in the SCC and UEA groups. ADC was significantly higher in the GAS group than in the SCC group (1.092×10-3 vs. 0.819×10-3 mm2/s). This study revealed that GAS is characterized by tumor presence in the entire cervix, infiltrative growth pattern, intrauterine fluid collection, and frequent microcyst or macrocyst formation. Moreover, ADC was significantly higher in the GAS group than in the SCC group.

Privacy-preserving blockchain-based federated learning for brain tumor segmentation

Improved data sharing between healthcare providers can lead to a higher probability of accurate diagnosis, more effective treatments, and enhanced capabilities of healthcare organizations. One critical area of focus is brain tumor segmentation, a complex task due to the heterogeneous appearance, irregular shape, and variable location of tumors. Accurate segmentation is essential for proper diagnosis and effective treatment planning, yet current techniques often fall short due to these complexities. However, the sensitive nature of health data often prohibits its sharing. Moreover, the healthcare industry faces significant issues, including preserving the privacy of the model and instilling trust in the model. This paper proposes a framework to address these privacy and trust issues by introducing a mechanism for training the global model using federated learning and sharing the encrypted learned parameters via a permissioned blockchain. The blockchain-federated learning algorithm we designed aggregates gradients in the permissioned blockchain to decentralize the global model, while the introduced masking approach retains the privacy of the model parameters. Unlike traditional raw data sharing, this approach enables hospitals or medical research centers to contribute to a globally learned model, thereby enhancing the performance of the central model for all participating medical entities. As a result, the global model can learn about several specific diseases and benefit each contributor with new disease diagnosis tasks, leading to improved treatment options. The proposed algorithm ensures the quality of model data when aggregating the local model, using an asynchronous federated learning procedure to evaluate the shared model’s quality. The experimental results demonstrate the efficacy of the proposed scheme for the critical and challenging task of brain tumor segmentation. Specifically, our method achieved a 1.99% improvement in Dice similarity coefficient for enhancing tumors and a 19.08% reduction in Hausdorff distance for whole tumors compared to the baseline methods, highlighting the significant advancement in segmentation performance and reliability.

Image-based biomarkers for engineering neuroblastoma patient-specific computational models

AbstractChildhood cancer is a devastating disease that requires continued research and improved treatment options to increase survival rates and quality of life for those affected. The response to cancer treatment can vary significantly among patients, highlighting the need for a deeper understanding of the underlying mechanisms involved in tumour growth and recovery to improve diagnostic and treatment strategies. Patient-specific models have emerged as a promising alternative to tackle the challenges in tumour mechanics through individualised simulation. In this study, we present a methodology to develop subject-specific tumour models, which incorporate the initial distribution of cell density, tumour vasculature, and tumour geometry obtained from clinical MRI imaging data. Tumour mechanics is simulated through the Finite Element method, coupling the dynamics of tumour growth and remodelling and the mechano-transport of oxygen and chemotherapy. These models enable a new application of tumour mechanics, namely predicting changes in tumour size and shape resulting from chemotherapeutic interventions for individual patients. Although the specific context of application in this work is neuroblastoma, the proposed methodologies can be extended to other solid tumours. Given the difficulty for treating paediatric solid tumours like neuroblastoma, this work includes two patients with different prognosis, who received chemotherapy treatment. The results obtained from the simulation are compared with the actual tumour size and shape from patients. Overall, the simulations provided clinically useful information to evaluate the effectiveness of the chemotherapy treatment in each case. These results suggest that the biomechanical model could be a valuable tool for personalised medicine in solid tumours.

Role of the Clinical Features and MRI Parameters on Ki-67 Expression in Hepatocellular Carcinoma Patients: Development of a Predictive Nomogram.

To develop a nomogram using clinical features and the MRI parameters for preoperatively predicting the expression of Ki-67 in patients with hepatocellular carcinoma (HCC). One hundred and forty patients (training cohorts: n = 108; validation cohorts: n = 32) with confirmed HCC were investigated. Mann-Whitney U test, independent sample t-test, and chi-squared test were used to analyze the continuous and categorical variables. Univariate and multivariate logistic regression analyses were performed to examine the clinical variables and parameters from MRI associated with Ki-67 expression. As a result, a nomogram was developed based on these associations in patients with HCC. The performance of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC) and calibration curves. In the training set, multivariable logistic regression analysis revealed that lens culinaris agglutinin-reactive fraction of alpha-fetoprotein (AFP-L3) levels, protein induced by vitamin K absence or antagonist-II (PIVKA-II) levels, and tumor shape were independent predictors for Ki-67 expression (p < 0.05). These three variables and the apparent diffusion coefficient (ADC) value were used to establish a nomogram, while the ADC value was found to be a marginal significant predictor. The model demonstrated a strong ability to discriminate Ki-67 expression in both the training and validation cohorts (AUC = 0.862, 0.877). A non-invasive preoperative prediction method, which incorporates MRI variables and clinical features was developed, and showed effectiveness in evaluating Ki-67 expression in HCC patients.

Optimized machine learning algorithm for thyroid tumour type classification: A hybrid approach Random Forest, and intelligent optimization algorithms

Thyroid tumours are a common form of cancer, and accurate classification of their type is crucial for effective treatment planning. This research presents a hybrid approach for the classification of thyroid tumours based on their type. The proposed approach combines the use of advanced machine learning techniques with a comprehensive database of thyroid tumour samples. The database includes various features such as tumour size, shape, and texture, as well as patient-specific information. The hybrid approach aims to optimize the classification process by leveraging the diverse set of features and utilizing the power of machine learning algorithms. By harnessing the power of machine learning algorithms, this approach has the potential to revolutionize the field of thyroid tumour classification and significantly improve patient outcomes. The optimization strategy is Particle Swarm Optimization, refining the classification performance and ensuring optimal accuracy in identifying and categorizing four types of thyroid tumours. The utilization of advanced diagnostic tools and state-of-the-art Random forest classifier techniques in this approach marks a significant advancement in the field of thyroid tumour classification. Through the augmentation of the dataset and the pre-processing techniques employed, the hybrid classification system demonstrates enhanced accuracy and reliability in distinguishing between different types of thyroid tumours. This innovative approach not only provides a more comprehensive understanding of thyroid tumours but also paves the way for personalized and effective treatment strategies, ultimately improving patient care and outcomes.