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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. Four-dimensional cone-beam computed tomography (4D-CBCT) can visualize moving tumors, thus adaptive radiation therapy (ART) could be improved if 4D-CBCT were used. However, 4D-CBCT images suffer from severe im...

    Authors: Keisuke Usui, Koichi Ogawa, Masami Goto, Yasuaki Sakano, Shinsuke Kyougoku and Hiroyuki Daida
    Citation: Radiation Oncology 2022 17:69
  2. The goal of this study is to develop and validate a radiomics nomogram integrating the radiomics features from DCE-MRI and clinical factors for the preoperative diagnosis of axillary lymph node (ALN) metastasi...

    Authors: Deling Song, Fei Yang, Yujiao Zhang, Yazhe Guo, Yingwu Qu, Xiaochen Zhang, Yuexiang Zhu and Shujun Cui
    Citation: Cancer Imaging 2022 22:17
  3. Automatic prostate tumor segmentation is often unable to identify the lesion even if multi-parametric MRI data is used as input, and the segmentation output is difficult to verify due to the lack of clinically...

    Authors: Deepa Darshini Gunashekar, Lars Bielak, Leonard Hägele, Benedict Oerther, Matthias Benndorf, Anca-L. Grosu, Thomas Brox, Constantinos Zamboglou and Michael Bock
    Citation: Radiation Oncology 2022 17:65
  4. This paper describes the development of a predicted electronic portal imaging device (EPID) transmission image (TI) using Monte Carlo (MC) and deep learning (DL). The measured and predicted TI were compared fo...

    Authors: Jun Zhang, Zhibiao Cheng, Ziting Fan, Qilin Zhang, Xile Zhang, Ruijie Yang and Junhai Wen
    Citation: Radiation Oncology 2022 17:31
  5. With the rapid growth of deep learning research for medical applications comes the need for clinical personnel to be comfortable and familiar with these techniques. Taking a proven approach, we developed a str...

    Authors: John C. Asbach, Anurag K. Singh, L. Shawn Matott and Anh H. Le
    Citation: Radiation Oncology 2022 17:28
  6. Accurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning. Manual segmentation of these tumors is time-consuming and vulnerable to inconsistencies bet...

    Authors: Jens P.E. Schouten, Samantha Noteboom, Roland M. Martens, Steven W. Mes, C. René Leemans, Pim de Graaf and Martijn D. Steenwijk
    Citation: Cancer Imaging 2022 22:8
  7. Magnetic Resonance Image guided Stereotactic body radiotherapy (MRgRT) is an emerging technology that is increasingly used in treatment of visceral cancers, such as pancreatic adenocarcinoma (PDAC). Given the ...

    Authors: M. R. Tomaszewski, K. Latifi, E. Boyer, R. F. Palm, I. El Naqa, E. G. Moros, S. E. Hoffe, S. A. Rosenberg, J. M. Frakes and R. J. Gillies
    Citation: Radiation Oncology 2021 16:237
  8. The Ki67 expression is associated with the advanced clinicopathological features and poor prognosis in bladder cancer (BCa). We aimed to develop and validate magnetic resonance imaging (MRI)-based radiomics si...

    Authors: Zongtai Zheng, Zhuoran Gu, Feijia Xu, Niraj Maskey, Yanyan He, Yang Yan, Tianyuan Xu, Shenghua Liu and Xudong Yao
    Citation: Cancer Imaging 2021 21:65
  9. To develop a nomogram model for predicting local progress-free survival (LPFS) in esophageal squamous cell carcinoma (ESCC) patients treated with concurrent chemo-radiotherapy (CCRT).

    Authors: He-San Luo, Ying-Ying Chen, Wei-Zhen Huang, Sheng-Xi Wu, Shao-Fu Huang, Hong-Yao Xu, Ren-Liang Xue, Ze-Sen Du, Xu-Yuan Li, Lian-Xin Lin and He-Cheng Huang
    Citation: Radiation Oncology 2021 16:201
  10. Cystic renal cell carcinoma (CRCC) and cystic collecting duct carcinoma (CCDC) share similar oncogeni and some imaging findings. The aim of this study was to characterize the clinical and CT imagings features ...

    Authors: Qingqiang Zhu, Jun Ling, Jing Ye, Wenrong Zhu, Jingtao Wu and Wenxin Chen
    Citation: Cancer Imaging 2021 21:52
  11. Histopathological classification of Wilms tumors determines treatment regimen. Machine learning has been shown to contribute to histopathological classification in various malignancies but requires large numbe...

    Authors: Jikke J. Rutgers, Tessa Bánki, Ananda van der Kamp, Tomas J. Waterlander, Marijn A. Scheijde-Vermeulen, Marry M. van den Heuvel-Eibrink, Jeroen A. W. M. van der Laak, Marta Fiocco, Annelies M. C. Mavinkurve-Groothuis and Ronald R. de Krijger
    Citation: Diagnostic Pathology 2021 16:77
  12. Performing Response Evaluation Criteria in Solid Tumor (RECISTS) measurement is a non-trivial task requiring much expertise and time. A deep learning-based algorithm has the potential to assist with rapid and ...

    Authors: MinJae Woo, A. Michael Devane, Steven C. Lowe, Ervin L Lowther and Ronald W. Gimbel
    Citation: Cancer Imaging 2021 21:43
  13. We recently described the validation of deep learning-based auto-segmented contour (DC) models for organs at risk (OAR) and clinical target volumes (CTV). In this study, we evaluate the performance of implemen...

    Authors: Jordan Wong, Vicky Huang, Derek Wells, Joshua Giambattista, Jonathan Giambattista, Carter Kolbeck, Karl Otto, Elantholi P. Saibishkumar and Abraham Alexander
    Citation: Radiation Oncology 2021 16:101
  14. Most MRI radiomics studies to date, even multi-centre ones, have used “pure” datasets deliberately accrued from single-vendor, single-field-strength scanners. This does not reflect aspirations for the ultimate...

    Authors: Simon J. Doran, Santosh Kumar, Matthew Orton, James d’Arcy, Fenna Kwaks, Elizabeth O’Flynn, Zaki Ahmed, Kate Downey, Mitch Dowsett, Nicholas Turner, Christina Messiou and Dow-Mu Koh
    Citation: Cancer Imaging 2021 21:37
  15. To generate and validate state-of-the-art radiomics models for prediction of radiation-induced lung injury and oncologic outcome in non-small cell lung cancer (NSCLC) patients treated with robotic stereotactic...

    Authors: Khaled Bousabarah, Oliver Blanck, Susanne Temming, Maria-Lisa Wilhelm, Mauritius Hoevels, Wolfgang W. Baus, Daniel Ruess, Veerle Visser-Vandewalle, Maximilian I. Ruge, Harald Treuer and Martin Kocher
    Citation: Radiation Oncology 2021 16:74
  16. Deep learning-based delineation of organs-at-risk for radiotherapy purposes has been investigated to reduce the time-intensiveness and inter-/intra-observer variability associated with manual delineation. We s...

    Authors: Ward van Rooij, Max Dahele, Hanne Nijhuis, Berend J. Slotman and Wilko F. Verbakel
    Citation: Radiation Oncology 2020 15:272
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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

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

  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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