keyword
https://read.qxmd.com/read/38535056/an-improved-skin-lesion-classification-using-a-hybrid-approach-with-active-contour-snake-model-and-lightweight-attention-guided-capsule-networks
#1
JOURNAL ARTICLE
Kavita Behara, Ernest Bhero, John Terhile Agee
Skin cancer is a prevalent type of malignancy on a global scale, and the early and accurate diagnosis of this condition is of utmost importance for the survival of patients. The clinical assessment of cutaneous lesions is a crucial aspect of medical practice, although it encounters several obstacles, such as prolonged waiting time and misinterpretation. The intricate nature of skin lesions, coupled with variations in appearance and texture, presents substantial barriers to accurate classification. As such, skilled clinicians often struggle to differentiate benign moles from early malignant tumors in skin images...
March 17, 2024: Diagnostics
https://read.qxmd.com/read/38534540/brain-tumor-detection-and-categorization-with-segmentation-of-improved-unsupervised-clustering-approach-and-machine-learning-classifier
#2
JOURNAL ARTICLE
Usharani Bhimavarapu, Nalini Chintalapudi, Gopi Battineni
There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive...
March 8, 2024: Bioengineering
https://read.qxmd.com/read/38527731/intra-and-peritumoral-pet-radiomics-analysis-to-predict-the-pathological-response-in-breast-cancer-patients-receiving-neoadjuvant-chemotherapy
#3
JOURNAL ARTICLE
Ayşegül Aksu, Güç Zeynep Gülsüm, Kadir Alper Küçüker, Ahmet Alacacıoğlu, Bülent Turgut
OBJECTIVE: The aim of our study was to evaluate the contribution of 18Fluorine-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) radiomic data obtained from both the tumoral and peritumoral area in predicting pathological complete response (pCR) in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC). METHODS: Female patients with a diagnosis of invasive ductal carcinoma who received NAC were evaluated retrospectively...
March 23, 2024: Revista española de medicina nuclear e imagen molecular
https://read.qxmd.com/read/38517821/prediction-of-the-benign-and-malignant-nature-of-masses-in-copd-background-based-on-habitat-based-enhanced-ct-radiomics-modeling-a-preliminary-study
#4
JOURNAL ARTICLE
Wanzhao Zuo, Jing Li, Mingyan Zuo, Miao Li, Shuang Zhou, Xing Cai
BACKGROUND: It is difficult to differentiate between chronic obstructive pulmonary disease (COPD)-peripheral bronchogenic carcinoma (COPD-PBC) and inflammatory masses. OBJECTIVE: This study aims to predict COPD-PBC based on clinical data and preoperative Habitat-based enhanced CT radiomics (HECT radiomics) modeling. METHODS: A retrospective analysis was conducted on clinical imaging data of 232 cases of postoperative pathological confirmed PBC or inflammatory masses...
February 29, 2024: Technology and Health Care: Official Journal of the European Society for Engineering and Medicine
https://read.qxmd.com/read/38514087/impact-of-18-f-fdg-pet-intensity-normalization-on-radiomic-features-of-oropharyngeal-squamous-cell-carcinomas-and-machine-learning-generated-biomarkers
#5
JOURNAL ARTICLE
Stefan P Haider, Tal Zeevi, Kariem Sharaf, Moritz Gross, Amit Mahajan, Benjamin H Kann, Benjamin L Judson, Manju L Prasad, Barbara Burtness, Mariam Aboian, Martin Canis, Christoph A Reichel, Philipp Baumeister, Seyedmehdi Payabvash
We aimed to investigate the effects of 18 F-FDG PET voxel intensity normalization on radiomic features of oropharyngeal squamous cell carcinoma (OPSCC) and machine learning-generated radiomic biomarkers. Methods: We extracted 1,037 18 F-FDG PET radiomic features quantifying the shape, intensity, and texture of 430 OPSCC primary tumors. The reproducibility of individual features across 3 intensity-normalized images (body-weight SUV, reference tissue activity ratio to lentiform nucleus of brain and cerebellum) and the raw PET data was assessed using an intraclass correlation coefficient (ICC)...
March 21, 2024: Journal of Nuclear Medicine
https://read.qxmd.com/read/38505880/the-role-of-artificial-intelligence-and-texture-analysis-in-interventional-radiological-treatments-of-liver-masses-a-narrative-review
#6
JOURNAL ARTICLE
Sonia Triggiani, Maria T Contaldo, Giulia Mastellone, Maurizio Cè, Anna M Ierardi, Gianpaolo Carrafiello, Michaela Cellina
Liver lesions, including both benign and malignant tumors, pose significant challenges in interventional radiological treatment planning and prognostication. The emerging field of artificial intelligence (AI) and its integration with texture analysis techniques have shown promising potential in predicting treatment outcomes, enhancing precision, and aiding clinical decision-making. This comprehensive review aims to summarize the current state-of-the-art research on the application of AI and texture analysis in determining treatment response, recurrence rates, and overall survival outcomes for patients undergoing interventional radiological treatment for liver lesions...
2024: Critical Reviews in Oncogenesis
https://read.qxmd.com/read/38486342/artificial-intelligence-based-mri-radiomics-and-radiogenomics-in-glioma
#7
REVIEW
Haiqing Fan, Yilin Luo, Fang Gu, Bin Tian, Yongqin Xiong, Guipeng Wu, Xin Nie, Jing Yu, Juan Tong, Xin Liao
The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of gliomas pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). Radiomics and radiogenomics present a potential to precisely diagnose and predict survival and treatment responses, via morphological, textural, and functional features derived from MRI data, as well as genomic data...
March 14, 2024: Cancer Imaging: the Official Publication of the International Cancer Imaging Society
https://read.qxmd.com/read/38475819/survival-time-prediction-in-patients-with-high-grade-serous-ovarian-cancer-based-on-18-f-fdg-pet-ct-derived-inter-tumor-heterogeneity-metrics
#8
JOURNAL ARTICLE
Dianning He, Xin Zhang, Zhihui Chang, Zhaoyu Liu, Beibei Li
BACKGROUND: The presence of heterogeneity is a significant attribute within the context of ovarian cancer. This study aimed to assess the predictive accuracy of models utilizing quantitative 18 F-FDG PET/CT derived inter-tumor heterogeneity metrics in determining progression-free survival (PFS) and overall survival (OS) in patients diagnosed with high-grade serous ovarian cancer (HGSOC). Additionally, the study investigated the potential correlation between model risk scores and the expression levels of p53 and Ki-67...
March 12, 2024: BMC Cancer
https://read.qxmd.com/read/38473363/pixelwise-gradient-model-with-gan-for-virtual-contrast-enhancement-in-mri-imaging
#9
JOURNAL ARTICLE
Ka-Hei Cheng, Wen Li, Francis Kar-Ho Lee, Tian Li, Jing Cai
Background : The development of advanced computational models for medical imaging is crucial for improving diagnostic accuracy in healthcare. This paper introduces a novel approach for virtual contrast enhancement (VCE) in magnetic resonance imaging (MRI), particularly focusing on nasopharyngeal cancer (NPC). Methods : The proposed model, Pixelwise Gradient Model with GAN for Virtual Contrast Enhancement (PGMGVCE), makes use of pixelwise gradient methods with Generative Adversarial Networks (GANs) to enhance T1-weighted (T1-w) and T2-weighted (T2-w) MRI images...
February 29, 2024: Cancers
https://read.qxmd.com/read/38472624/magnetic-resonance-imaging-based-radiomics-analysis-of-the-differential-diagnosis-of-ovarian-clear-cell-carcinoma-and-endometrioid-carcinoma-a-retrospective-study
#10
JOURNAL ARTICLE
Nobuyuki Takeyama, Yasushi Sasaki, Yasuo Ueda, Yuki Tashiro, Eliko Tanaka, Kyoko Nagai, Miki Morioka, Takafumi Ogawa, Genshu Tate, Toshi Hashimoto, Yoshimitsu Ohgiya
PURPOSE: To retrospectively evaluate the diagnostic potential of magnetic resonance imaging (MRI)-based features and radiomics analysis (RA)-based features for discriminating ovarian clear cell carcinoma (CCC) from endometrioid carcinoma (EC). MATERIALS AND METHODS: Thirty-five patients with 40 ECs and 42 patients with 43 CCCs who underwent pretherapeutic MRI examinations between 2011 and 2022 were enrolled. MRI-based features of the two groups were compared. RA-based features were extracted from the whole tumor volume on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (cT1WI), and apparent diffusion coefficient (ADC) maps...
March 12, 2024: Japanese Journal of Radiology
https://read.qxmd.com/read/38472447/ct-based-pancreatic-radiomics-predicts-secondary-loss-of-response-to-infliximab-in-biologically-na%C3%A3-ve-patients-with-crohn-s-disease
#11
JOURNAL ARTICLE
Tian Yang, Jing Feng, Ruchen Yao, Qi Feng, Jun Shen
OBJECTIVES: Predicting secondary loss of response (SLR) to infliximab (IFX) is paramount for tailoring personalized management regimens. Concurrent pancreatic manifestations in patients with Crohn's disease (CD) may correlate with SLR to anti-tumor necrosis factor treatment. This work aimed to evaluate the potential of pancreatic radiomics to predict SLR to IFX in biologic-naive individuals with CD. METHODS: Three models were developed by logistic regression analyses to identify high-risk subgroup prone to SLR...
March 13, 2024: Insights Into Imaging
https://read.qxmd.com/read/38468050/fractal-based-analysis-of-histological-features-of-brain-tumors
#12
JOURNAL ARTICLE
Omar S Al-Kadi, Antonio Di Ieva
The structural complexity of brain tumor tissue represents a major challenge for effective histopathological diagnosis. Tumor vasculature is known to be heterogeneous, and mixtures of patterns are usually present. Therefore, extracting key descriptive features for accurate quantification is not a straightforward task. Several steps are involved in the texture analysis process where tissue heterogeneity contributes to the variability of the results. One of the interesting aspects of the brain lies in its fractal nature...
2024: Advances in Neurobiology
https://read.qxmd.com/read/38468048/texture-estimation-for-abnormal-tissue-segmentation-in-brain-mri
#13
JOURNAL ARTICLE
Syed M S Reza, Atiq Islam, Khan M Iftekharuddin
This chapter discusses multifractal texture estimation and characterization of brain lesions (necrosis, edema, enhanced tumor, nonenhanced tumor, etc.) in magnetic resonance (MR) images. This work formulates the complex texture of tumor in MR images using a stochastic model known as multifractional Brownian motion (mBm). Mathematical derivations of the mBm model and corresponding algorithm to extract the spatially varying multifractal texture feature are discussed. Extracted multifractal texture feature is fused with other effective features to enhance the tissue characteristics...
2024: Advances in Neurobiology
https://read.qxmd.com/read/38464383/quantitative-peritumoral-magnetic-resonance-imaging-fingerprinting-improves-machine-learning-based-prediction-of-overall-survival-in-colorectal-cancer
#14
JOURNAL ARTICLE
Azadeh Tabari, Brian D'Amore, Janice Noh, Michael S Gee, Dania Daye
AIM: To investigate magnetic resonance imaging (MRI)-based peritumoral texture features as prognostic indicators of survival in patients with colorectal liver metastasis (CRLM). METHODS: From 2007-2015, forty-eight patients who underwent MRI within 3 months prior to initiating treatment for CRLM were identified. Clinicobiological prognostic variables were obtained from electronic medical records. Ninety-four metastatic hepatic lesions were identified on T1-weighted post-contrast images and volumetrically segmented...
2024: Exploration of targeted anti-tumor therapy
https://read.qxmd.com/read/38460312/developing-a-novel-image-marker-to-predict-the-clinical-outcome-of-neoadjuvant-chemotherapy-nact-for-ovarian-cancer-patients
#15
JOURNAL ARTICLE
Ke Zhang, Neman Abdoli, Patrik Gilley, Youkabed Sadri, Xuxin Chen, Theresa C Thai, Lauren Dockery, Kathleen Moore, Robert S Mannel, Yuchen Qiu
OBJECTIVE: Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the clinical outcomes to NACT vary significantly among different subgroups. Partial responses to NACT may lead to suboptimal debulking surgery, which will result in adverse prognosis. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy prognosis prediction of NACT at an early stage...
February 27, 2024: Computers in Biology and Medicine
https://read.qxmd.com/read/38459590/spatial-intratumor-heterogeneity-of-programmed-death-ligand-1-expression-predicts-poor-prognosis-in-resected-non-small-cell-lung-cancer
#16
JOURNAL ARTICLE
Yusuke Nagasaki, Tetsuro Taki, Kotaro Nomura, Kenta Tane, Tomohiro Miyoshi, Joji Samejima, Keiju Aokage, Seiyu Jeong-Yoo Ohtani-Kim, Motohiro Kojima, Shingo Sakashita, Naoya Sakamoto, Shumpei Ishikawa, Kenji Suzuki, Masahiro Tsuboi, Genichiro Ishii
OBJECTIVE: We quantified the pathological spatial intratumor heterogeneity (ITH) of programmed death-ligand 1 (PD-L1) expression and investigated its relevance to patient outcomes in surgically resected non-small cell lung carcinoma (NSCLC). MATERIALS AND METHODS: This study enrolled 239 consecutive surgically resected NSCLC specimens of pathological stage IIA-IIIB. To characterize the spatial ITH of PD-L1 expression in NSCLC tissues, we developed a mathematical model based on texture image analysis and determined the spatial heterogeneity index of PD-L1 (SHIP) for each tumor...
March 8, 2024: Journal of the National Cancer Institute
https://read.qxmd.com/read/38457590/intelligent-diagnosis-of-lung-nodule-images-based-on-machine-learning-in-the-context-of-lung-teaching
#17
JOURNAL ARTICLE
Miaomiao Li, Lilei Zhuang, Sheng Hu, Li Sun, Yangxiang Liu, Zhengwei Dou, Tao Jiang
The vast majority of intelligent diagnosis models have widespread problems, which seriously affect the medical staff judgment of patients' injuries. So depending on the situation, you need to use different algorithms, The study suggests a model for intelligent diagnosis of lung nodule images based on machine learning, and a support vector machine-based machine learning algorithm is selected. In order to improve the diagnostic accuracy of intelligent diagnosis of lung nodule images as well as the diagnostic model of lung nodule images...
March 8, 2024: Medicine (Baltimore)
https://read.qxmd.com/read/38448145/a-journey-from-omics-to-clinicomics-in-solid-cancers-success-stories-and-challenges
#18
JOURNAL ARTICLE
Sanjana Mehrotra, Sankalp Sharma, Rajeev Kumar Pandey
The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease...
2024: Advances in Protein Chemistry and Structural Biology
https://read.qxmd.com/read/38440396/a-robust-approach-for-multi-type-classification-of-brain-tumor-using-deep-feature-fusion
#19
JOURNAL ARTICLE
Wenna Chen, Xinghua Tan, Jincan Zhang, Ganqin Du, Qizhi Fu, Hongwei Jiang
Brain tumors can be classified into many different types based on their shape, texture, and location. Accurate diagnosis of brain tumor types can help doctors to develop appropriate treatment plans to save patients' lives. Therefore, it is very crucial to improve the accuracy of this classification system for brain tumors to assist doctors in their treatment. We propose a deep feature fusion method based on convolutional neural networks to enhance the accuracy and robustness of brain tumor classification while mitigating the risk of over-fitting...
2024: Frontiers in Neuroscience
https://read.qxmd.com/read/38436433/classification-of-multi-feature-fusion-ultrasound-images-of-breast-tumor-within-category-4-using-convolutional-neural-networks
#20
JOURNAL ARTICLE
Pengfei Xu, Jing Zhao, Mingxi Wan, Qing Song, Qiang Su, Diya Wang
BACKGROUND: Breast tumor is a fatal threat to the health of women. Ultrasound (US) is a common and economical method for the diagnosis of breast cancer. Breast imaging reporting and data system (BI-RADS) category 4 has the highest false-positive value of about 30% among five categories. The classification task in BI-RADS category 4 is challenging and has not been fully studied. PURPOSE: This work aimed to use convolutional neural networks (CNNs) for breast tumor classification using B-mode images in category 4 to overcome the dependence on operator and artifacts...
March 4, 2024: Medical Physics
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