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Artificial intelligence in Cancer imaging and diagnosis

Diagnostic laboratories are in the midst of a transformation and are somewhat at cross-roads. In the face of decreasing revenues and increasing workloads, there is a rise in demand to increase throughput and efficiency while maintaining or improving quality, particularly in clinical diagnostics.  In addition, today’s complex mix of therapies offered to a varied demographic and the shift toward precision medicine implies that oncologists and pathologists must work in concert to target the right patient for the right therapy at the right time. 

New tools and technologies such as computational and digital pathology, molecular diagnostics and artificial intelligence (AI) are making their way into advanced clinical diagnostics, providing some unique opportunities to incorporate these tools into the evolving health care landscape.  Herein we present a cross journal series with articles that would give the viewer a perspective of the current trends and future prospects of AI primarily in clinical diagnostics.   

  1. Preoperative prediction of the Lauren classification in gastric cancer (GC) is very important to the choice of therapy, the evaluation of prognosis, and the improvement of quality of life. However, there is no...

    Authors: Xiao-Xiao Wang, Yi Ding, Si-Wen Wang, Di Dong, Hai-Lin Li, Jian Chen, Hui Hu, Chao Lu, Jie Tian and Xiu-Hong Shan

    Citation: Cancer Imaging 2020 20:83

    Content type: Research article

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  2. Laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) with thyroid cartilage invasion are considered T4 and need total laryngectomy. However, the accuracy of preoperative diagnosis of thyroid cartilage ...

    Authors: Ran Guo, Jian Guo, Lichen Zhang, Xiaoxia Qu, Shuangfeng Dai, Ruchen Peng, Vincent F. H. Chong and Junfang Xian

    Citation: Cancer Imaging 2020 20:81

    Content type: Research article

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  3. Recently, radiomic texture quantification of tumors has received much attention from radiologists, scientists, and stakeholders because several results have shown the feasibility of using the technique to diag...

    Authors: Ismail Bilal Masokano, Wenguang Liu, Simin Xie, Dama Faniriantsoa Henrio Marcellin, Yigang Pei and Wenzheng Li

    Citation: Cancer Imaging 2020 20:67

    Content type: Review

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  4. To establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast...

    Authors: Meijie Liu, Ning Mao, Heng Ma, Jianjun Dong, Kun Zhang, Kaili Che, Shaofeng Duan, Xuexi Zhang, Yinghong Shi and Haizhu Xie

    Citation: Cancer Imaging 2020 20:65

    Content type: Research article

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  5. This study aims to identify robust radiomic features for Magnetic Resonance Imaging (MRI), assess feature selection and machine learning methods for overall survival classification of Glioblastoma multiforme p...

    Authors: Yannick Suter, Urspeter Knecht, Mariana Alão, Waldo Valenzuela, Ekkehard Hewer, Philippe Schucht, Roland Wiest and Mauricio Reyes

    Citation: Cancer Imaging 2020 20:55

    Content type: Research article

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  6. Automatic tumor segmentation based on Convolutional Neural Networks (CNNs) has shown to be a valuable tool in treatment planning and clinical decision making. We investigate the influence of 7 MRI input channe...

    Authors: Lars Bielak, Nicole Wiedenmann, Arnie Berlin, Nils Henrik Nicolay, Deepa Darshini Gunashekar, Leonard Hägele, Thomas Lottner, Anca-Ligia Grosu and Michael Bock

    Citation: Radiation Oncology 2020 15:181

    Content type: Research

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  7. Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. The classification of each cell type requires detection of uniquely colo...

    Authors: Danielle J. Fassler, Shahira Abousamra, Rajarsi Gupta, Chao Chen, Maozheng Zhao, David Paredes, Syeda Areeha Batool, Beatrice S. Knudsen, Luisa Escobar-Hoyos, Kenneth R. Shroyer, Dimitris Samaras, Tahsin Kurc and Joel Saltz

    Citation: Diagnostic Pathology 2020 15:100

    Content type: Research

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    The Publisher Correction to this article has been published in Diagnostic Pathology 2020 15:116

  8. Identification of bladder layers is a necessary prerequisite to bladder cancer diagnosis and prognosis. We present a method of multi-class image segmentation, which recognizes urothelium, lamina propria, muscu...

    Authors: Muhammad Khalid Khan Niazi, Enes Yazgan, Thomas E. Tavolara, Wencheng Li, Cheryl T. Lee, Anil Parwani and Metin N. Gurcan

    Citation: Diagnostic Pathology 2020 15:87

    Content type: Research

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  9. To develop a radiomics nomogram based on computed tomography (CT) images that can help differentiate lung adenocarcinomas and granulomatous lesions appearing as sub-centimeter solid nodules (SCSNs).

    Authors: Xiangmeng Chen, Bao Feng, Yehang Chen, Kunfeng Liu, Kunwei Li, Xiaobei Duan, Yixiu Hao, Enming Cui, Zhuangsheng Liu, Chaotong Zhang, Wansheng Long and Xueguo Liu

    Citation: Cancer Imaging 2020 20:45

    Content type: Research article

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  10. The mitotic count in breast carcinoma is an important prognostic marker. Unfortunately substantial inter- and intra-laboratory variation exists when pathologists manually count mitotic figures. Artificial inte...

    Authors: Liron Pantanowitz, Douglas Hartman, Yan Qi, Eun Yoon Cho, Beomseok Suh, Kyunghyun Paeng, Rajiv Dhir, Pamela Michelow, Scott Hazelhurst, Sang Yong Song and Soo Youn Cho

    Citation: Diagnostic Pathology 2020 15:80

    Content type: Research

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  11. The scoring of Ki-67 is highly relevant for the diagnosis, classification, prognosis, and treatment in breast invasive ductal carcinoma (IDC). Traditional scoring method of Ki-67 staining followed by manual co...

    Authors: Min Feng, Yang Deng, Libo Yang, Qiuyang Jing, Zhang Zhang, Lian Xu, Xiaoxia Wei, Yanyan Zhou, Diwei Wu, Fei Xiang, Yizhe Wang, Ji Bao and Hong Bu

    Citation: Diagnostic Pathology 2020 15:65

    Content type: Research

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  12. Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method...

    Authors: Ekin Ermiş, Alain Jungo, Robert Poel, Marcela Blatti-Moreno, Raphael Meier, Urspeter Knecht, Daniel M. Aebersold, Michael K. Fix, Peter Manser, Mauricio Reyes and Evelyn Herrmann

    Citation: Radiation Oncology 2020 15:100

    Content type: Research

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  13. Preoperative detection of lymph node (LN) metastasis is critical for planning treatments in colon cancer (CC). The clinical diagnostic criteria based on the size of the LNs are not sensitive to determine metas...

    Authors: Aydin Eresen, Yu Li, Jia Yang, Junjie Shangguan, Yury Velichko, Vahid Yaghmai, Al B. Benson III and Zhuoli Zhang

    Citation: Cancer Imaging 2020 20:30

    Content type: Research article

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  14. We developed a computational model integrating clinical data and imaging features extracted from contrast-enhanced computed tomography (CECT) images, to predict lymph node (LN) metastasis in patients with panc...

    Authors: Ke Li, Qiandong Yao, Jingjing Xiao, Meng Li, Jiali Yang, Wenjing Hou, Mingshan Du, Kang Chen, Yuan Qu, Lian Li, Jing Li, Xianqi Wang, Haoran Luo, Jia Yang, Zhuoli Zhang and Wei Chen

    Citation: Cancer Imaging 2020 20:12

    Content type: Research article

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