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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.   

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  1. 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 Published on:

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

  2. 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 Published on:

  3. 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 Published on:

  4. 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 Published on:

  5. 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 Published on:

  6. 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 Published on:

  7. 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 Published on:

  8. 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 Published on: