Cancer Investigation最新文献

Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Analysis of Saliva as a Diagnostic Specimen for Rapid Classification of Oral Squamous Cell Carcinoma Using Chemometrics Methods. 使用化学计量学方法对作为诊断样本的唾液进行衰减全反射-傅立叶变换红外光谱分析，以快速对口腔鳞状细胞癌进行分类。

IF 1.8 4区医学

Cancer Investigation Pub Date : 2024-10-01 DOI: 10.1080/07357907.2024.2403086

Mohammad Mahdi Khanmohammadi Khorrami, Nozhan Azimi, Maryam Koopaie, Mahsa Mohammadi, Soheila Manifar, Mohammadreza Khanmohammadi Khorrami

{"title":"Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Analysis of Saliva as a Diagnostic Specimen for Rapid Classification of Oral Squamous Cell Carcinoma Using Chemometrics Methods.","authors":"Mohammad Mahdi Khanmohammadi Khorrami, Nozhan Azimi, Maryam Koopaie, Mahsa Mohammadi, Soheila Manifar, Mohammadreza Khanmohammadi Khorrami","doi":"10.1080/07357907.2024.2403086","DOIUrl":"https://doi.org/10.1080/07357907.2024.2403086","url":null,"abstract":"Background & aim: Recent advancements in analytical techniques have highlighted the potential of Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy as a quick, cost-effective, non-invasive, and efficient tool for cancer diagnosis. This study aims to evaluate the effectiveness of ATR-FTIR spectroscopy in combination with supervised machine learning classification models for diagnosing OSCC using saliva samples.Methods & materials: Eighty unstimulated whole saliva samples from OSCC patients and healthy controls were collected. The ATR-FTIR spectroscopy was performed and spectral data were used to classify healthy and OSCC groups. The data were analyzed using machine learning classification methods such as Partial Least Squares-Discriminant Analysis (PLS-DA) and Support Vector Machine Classification (SVM-C). The classification performance of the models was evaluated by computing sensitivity, specificity, precision, and accuracy.Results: The samples were classified into two classes based on their spectral data. The obtained results demonstrate a high level of accuracy in the prediction sets of the PLS-DA and SVM-C models, with accuracy values of 0.960 and 0.962, respectively. The OSCC group sensitivity values for both PLS-DA and SVM-C models was 1.00, respectively.Conclusion: The study indicates that ATR-FTIR spectroscopy, combined with chemometrics, is a potential method for the non-invasive diagnosis of OSCC using saliva samples. This method achieved high accuracy and the findings of this study suggest that ATR-FTIR spectroscopy could be further developed for clinical applications in OSCC diagnosis.","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142361174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Use of Antiperspirant Products and Risk of Breast Cancer: A Meta-Analysis of Case-Control Studies. 使用止汗剂产品与乳腺癌风险：病例对照研究的元分析》（Meta-Analysis of Case-Control Studies）。

IF 1.8 4区医学

Cancer Investigation Pub Date : 2024-09-26 DOI: 10.1080/07357907.2024.2405864

Thao Thi Kim Trinh, Seung-Kwon Myung, Tien Hoang Tran, Kui Son Choi

引用次数: 0

Glucose Metabolism and Glucose Transporters in Head and Neck Squamous Cell Carcinoma. 头颈部鳞状细胞癌中的葡萄糖代谢和葡萄糖转运体

IF 1.8 4区医学

Cancer Investigation Pub Date : 2024-09-26 DOI: 10.1080/07357907.2024.2407424

Yanyan Ye, Zaizai Cao

引用次数: 0

Identification of Immune-Related Gene Pair Signature to Predict Prognosis of Diffuse Large B-Cell Lymphoma Based on Bioinformatics Analyses. 基于生物信息学分析鉴定用于预测弥漫大 B 细胞淋巴瘤预后的免疫相关基因对签名

IF 1.8 4区医学

Cancer Investigation Pub Date : 2024-09-23 DOI: 10.1080/07357907.2024.2405184

Zeinab Davoodi-Moghaddam, Farideh Jafari-Raddani, Davood Bashash

引用次数: 0

Flamingo Search Sailfish Optimizer Based SqueezeNet for Detection of Breast Cancer Using MRI Images. 基于 Flamingo Search Sailfish Optimizer 的 SqueezeNet 利用核磁共振成像检测乳腺癌。

IF 1.8 4区医学

Cancer Investigation Pub Date : 2024-09-20 DOI: 10.1080/07357907.2024.2403088

P Vijaya, Satish Chander, Roshan Fernandes, Anisha P Rodrigues, Maheswari Raja

{"title":"Flamingo Search Sailfish Optimizer Based SqueezeNet for Detection of Breast Cancer Using MRI Images.","authors":"P Vijaya, Satish Chander, Roshan Fernandes, Anisha P Rodrigues, Maheswari Raja","doi":"10.1080/07357907.2024.2403088","DOIUrl":"https://doi.org/10.1080/07357907.2024.2403088","url":null,"abstract":"Breast cancer with increased risk in women is identified with Breast Magnetic Resonance Imaging (Breast MRI) and this helps in evaluating treatment therapies. Breast MRI is time time-consuming process that involves the assessment of current imaging. This research work depends on the detection of breast cancer at the earlier stages. Among various cancers, breast cancer in women occurs in larger accounts for almost 30% of estimated cancer cases. In this research, many steps are followed for breast cancer detection like pre-processing, segmentation, augmentation, extraction of features, and cancer detection. Here, the median filter is utilized for pre-processing, as well as segmentation is followed after pre-processing, which is done by Psi-Net. Moreover, the process of augmentation like shearing, translation, and cropping are followed after segmentation. Also, the segmented image tends to process feature extraction, where features like shape features, Completed Local Binary Pattern (CLBP), Pyramid Histogram of Oriented Gradients (PHOG), and statistical features are extracted. Finally, breast cancer is detected using the DL model, SqueezeNet. Here, the newly devised Flamingo Search SailFish Optimizer (FSSFO) is used in training Psi-Net as well as SqueezeNet. Furthermore, FSSFO is the combination of both the Flamingo Search Algorithm (FSA) and SailFish Optimizer (SFO).","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142280525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficacy and Safety of First-Line Platinum-Based Doublet Chemotherapy in Advanced Primary Pulmonary Salivary Gland Tumors (PSGTs). 晚期原发性肺唾液腺肿瘤（PSGTs）一线铂基双联化疗的有效性和安全性

IF 1.8 4区医学

Cancer Investigation Pub Date : 2024-09-16 DOI: 10.1080/07357907.2024.2399046

Zheng Shi, Xiaohong Zeng, Wei Sun, Manyi Xu, Keda Shao, Jingwen Wei, Chunwei Xu, Zhengbo Song

引用次数: 0

The Relationship Between Body Image and Meaning of Life Among Women with Breast Cancer in Kerman, Iran. 伊朗克尔曼患乳腺癌妇女的身体形象与生活意义之间的关系。

IF 1.8 4区医学

Cancer Investigation Pub Date : 2024-09-01 Epub Date: 2024-08-07 DOI: 10.1080/07357907.2024.2371369

Mina Mahmoodabadi, Zohreh Khoshnood, Behjat Kalantari Khandani

引用次数: 0

TTYH3 Promotes Cervical Cancer Progression by Activating the Wnt/β-Catenin Signaling Pathway. TTYH3通过激活Wnt/β-Catenin信号通路促进宫颈癌进展

IF 1.8 4区医学

Cancer Investigation Pub Date : 2024-09-01 Epub Date: 2024-08-27 DOI: 10.1080/07357907.2024.2395014

Xiuyan Huang, Qing Li, Xiaoxia Zheng, Chen Jiang

引用次数: 0

Liver Cancer Diagnosis: Enhanced Deep Maxout Model with Improved Feature Set. 肝癌诊断：改进特征集的增强型深度 Maxout 模型

IF 1.8 4区医学

Cancer Investigation Pub Date : 2024-09-01 Epub Date: 2024-08-27 DOI: 10.1080/07357907.2024.2391359

Vinnakota Sai Durga Tejaswi, Venubabu Rachapudi

{"title":"Liver Cancer Diagnosis: Enhanced Deep Maxout Model with Improved Feature Set.","authors":"Vinnakota Sai Durga Tejaswi, Venubabu Rachapudi","doi":"10.1080/07357907.2024.2391359","DOIUrl":"10.1080/07357907.2024.2391359","url":null,"abstract":"This work proposed a liver cancer classification scheme that includes Preprocessing, Feature extraction, and classification stages. The source images are pre-processed using Gaussian filtering. For segmentation, this work proposes a LUV transformation-based adaptive thresholding-based segmentation process. After the segmentation, certain features are extracted that include multi-texon based features, Improved Local Ternary Pattern (LTP-based features), and GLCM features during this phase. In the Classification phase, an improved Deep Maxout model is proposed for liver cancer detection. The adopted scheme is evaluated over other schemes based on various metrics. While the learning rate is 60%, an improved deep maxout model achieved a higher F-measure value (0.94) for classifying liver cancer; however, the previous method like Support Vector Machine (SVM), Random Forest (RF), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), K-Nearest Neighbor (KNN), Deep maxout, Convolutional Neural Network (CNN), and DL model holds less F-measure value. An improved deep maxout model achieved minimal False Positive Rate (FPR), and False Negative Rate (FNR) values with the best outcomes compared to other existing models for liver cancer classification.","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142072104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cognitive Dysfunction in Non-CNS Metastatic Cancer: Comparing Brain Metastasis, Non-CNS Metastasis, and Healthy Controls. 非中枢神经系统转移性癌症的认知功能障碍：比较脑转移癌、非中枢神经系统转移癌和健康对照组

IF 1.8 4区医学

Cancer Investigation Pub Date : 2024-09-01 Epub Date: 2024-07-15 DOI: 10.1080/07357907.2024.2371368

Christopher Collette, Gabrielle Willhelm, Victor A Del Bene, Stephen L Aita, Dario Marotta, Terina Myers, Joseph Anderson, Meredith Gammon, Adam Gerstenecker, L Burt Nabors, John Fiveash, Kristen L Triebel

引用次数: 0