Cancer Investigation最新文献_第2页

Artificial intelligence in Cancer Clinical Research: IV. Inherent Limitations of Artificial Intelligence. 癌症临床研究中的人工智能：IV.人工智能固有的局限性。

IF 1.8 4区医学

Cancer Investigation Pub Date : 2024-10-01 Epub Date: 2024-10-11 DOI: 10.1080/07357907.2024.2412449

Gary H Lyman, Nicole M Kuderer

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 : 2024-10-01 Epub 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":"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":" ","pages":"745-768"},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","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

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":" ","pages":"710-725"},"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

Characteristics of Invasive Cribriform Carcinoma. 浸润性楔形细胞癌的特征

IF 1.8 4区医学

Cancer Investigation Pub Date : 2024-09-01 Epub Date: 2024-07-26 DOI: 10.1080/07357907.2024.2383930

Ryusei Yoshino, Masaki Nakatsubo, Nanami Ujiie, Akane Ito, Nana Yoshida, Masahiro Kitada

引用次数: 0

The Landscape and Prognosis of Microsatellite Stable (MSS) Esophageal, Gastro-Esophageal Junction and Gastric Adenocarcinomas with High Tumor Mutation Burden (TMB). 肿瘤突变负荷（TMB）较高的微卫星稳定（MSS）食管癌、胃食管交界癌和胃腺癌的情况和预后。

IF 1.8 4区医学

Cancer Investigation Pub Date : 2024-09-01 Epub Date: 2024-08-08 DOI: 10.1080/07357907.2024.2388107

Ioannis A Voutsadakis

{"title":"The Landscape and Prognosis of Microsatellite Stable (MSS) Esophageal, Gastro-Esophageal Junction and Gastric Adenocarcinomas with High Tumor Mutation Burden (TMB).","authors":"Ioannis A Voutsadakis","doi":"10.1080/07357907.2024.2388107","DOIUrl":"10.1080/07357907.2024.2388107","url":null,"abstract":"Background: A minority of patients with MSS tumors present a high tumor mutation burden (TMB) without underlying MMR defects.Methods: Publicly available genomic series were assessed for identification of patients with MSS gastric gastroesophageal junction, and esophageal adenocarcinomas and a high TMB, defined as more than 10 mutations per Mb. These were compared with MSS cancers and a low TMB for genetic alterations and for survival outcomes.Results: Patients with MSS cancers with high TMB in the MSK series were older but did not differ in other clinicopathologic parameters compared with MSS patients with low TMB. Mutations in tumor suppressors TP53 and APC and oncogenes KRAS and ERBB4 as well as amplifications of ERBB2 were more prevalent in the high TMB group of MSS cancers. Mutations in DDR associated genes, in epigenetic modifiers and in genes associated with immune response were more prevalent in the hIgh TMB group patients. However, high TMB was not associated with an improved survival in MSS gastric/gastroesophageal junction/esophageal adenocarcinomas (Log Rank p = 0.5).Conclusion: MSS Gastric/gastroesophageal junction/esophageal adenocarcinomas with TMB above 10 mutations per Mb possess a genomic landscape with increased alteration frequencies in common gastroesophageal cancer genes and pathways.","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"697-709"},"PeriodicalIF":1.8,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141901040","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

Does Aspirin Use Reduce the Risk for Ovarian Cancer? 服用阿司匹林会降低罹患卵巢癌的风险吗？

IF 1.8 4区医学

Cancer Investigation Pub Date : 2024-08-01 Epub Date: 2024-07-05 DOI: 10.1080/07357907.2024.2375573

John P Micha, Mark A Rettenmaier, Randy D Bohart, Bram H Goldstein

引用次数: 0

Predictive Modeling of Long-Term Prognosis After Resection in Typical Pulmonary Carcinoid: A Machine Learning Perspective. 典型肺癌切除术后长期预后的预测模型：机器学习视角

IF 1.8 4区医学

Cancer Investigation Pub Date : 2024-08-01 Epub Date: 2024-07-15 DOI: 10.1080/07357907.2024.2356002

Min Liang, Jian Huang, Caiyan Liu, Mafeng Chen

{"title":"Predictive Modeling of Long-Term Prognosis After Resection in Typical Pulmonary Carcinoid: A Machine Learning Perspective.","authors":"Min Liang, Jian Huang, Caiyan Liu, Mafeng Chen","doi":"10.1080/07357907.2024.2356002","DOIUrl":"10.1080/07357907.2024.2356002","url":null,"abstract":"Typical Pulmonary Carcinoid (TPC) is defined by its slow growth, frequently necessitating surgical intervention. Despite this, the long-term outcomes following tumor resection are not well understood. This study examined the factors impacting Overall Survival (OS) in patients with TPC, leveraging data from the Surveillance, Epidemiology, and End Results database spanning from 2000 to 2018. We employed Lasso-Cox analysis to identify prognostic features and developed various models using Random Forest, XGBoost, and Cox regression algorithms. Subsequently, we assessed model performance using metrics such as Area Under the Curve (AUC), calibration plot, Brier score, and Decision Curve Analysis (DCA). Among the 2687 patients, we identified five clinical features significantly affecting OS. Notably, the Random Forest model exhibited strong performance, achieving 5- and 7-year AUC values of 0.744/0.757 in the training set and 0.715/0.740 in the validation set, respectively, outperforming other models. Additionally, we developed a web-based platform aimed at facilitating easy access to the model. This study presents a machine learning model and a web-based support system for healthcare professionals, assisting in personalized treatment decisions for patients with TPC post-tumor resection.","PeriodicalId":9463,"journal":{"name":"Cancer Investigation","volume":" ","pages":"544-558"},"PeriodicalIF":1.8,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141615925","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