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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. Nearly one fourth of patients with pancreatic ductal adenocarcinoma (PDAC) occur to liver metastasis after surgery, and liver metastasis is a risk factor for prognosis for those patients with surgery therapy. ...

    Authors: Yuzhou Huang, Shurui Zhou, Yanji Luo, Jinmao Zou, Yaqing Li, Shaojie Chen, Ming Gao, Kaihong Huang and Guoda Lian
    Citation: Radiation Oncology 2023 18:79
  2. Long-term follow-up using volumetric measurement could significantly assist in the management of vestibular schwannomas (VS). Manual segmentation of VS from MRI for treatment planning and follow-up assessment ...

    Authors: Hesheng Wang, Tanxia Qu, Kenneth Bernstein, David Barbee and Douglas Kondziolka
    Citation: Radiation Oncology 2023 18:78
  3. This study leverages a large retrospective cohort of head and neck cancer patients in order to develop machine learning models to predict radiation induced hyposalivation from dose-volume histograms of the par...

    Authors: Derek K. Smith, Haley Clark, Allan Hovan and Jonn Wu
    Citation: Radiation Oncology 2023 18:77
  4. Mucinous carcinoma (MC) is a histological subtype of ovarian cancer that has a worse prognosis at advanced stages than the most prevalent histological subtype, high-grade serous carcinomas. Invasive patterns h...

    Authors: Taira Hada, Morikazu Miyamoto, Yuka Ohtsuka, Jin Suminokura, Tsubasa Ito, Naohisa Kishimoto, Soko Nishitani, Minori Takada, Akari Imauji, Risa Tanabe and Masashi Takano
    Citation: Diagnostic Pathology 2023 18:49
  5. To establish a novel model using radiomics analysis of pre-treatment and post-treatment magnetic resonance (MR) images for prediction of progression-free survival in the patients with stage II–IVA nasopharynge...

    Authors: Mi-Xue Sun, Meng-Jing Zhao, Li-Hao Zhao, Hao-Ran Jiang, Yu-Xia Duan and Gang Li
    Citation: Radiation Oncology 2023 18:67
  6. Low-grade papillary Schneiderian carcinoma (LGPSC) is a relatively new entity of the sinonasal tract and is characterized by a bland morphology simulating sinonasal papilloma, invasive growth pattern with push...

    Authors: Sayaka Yuzawa, Tomohiko Michizuka, Rika Kakisaka, Yusuke Ono, Manami Hayashi, Miki Takahara, Akihiro Katada, Yusuke Mizukami and Mishie Tanino
    Citation: Diagnostic Pathology 2023 18:44
  7. Artificial intelligence-based tools can be leveraged to improve detection and segmentation of brain metastases for stereotactic radiosurgery (SRS). VBrain by Vysioneer Inc. is a deep learning algorithm with re...

    Authors: Jen-Yeu Wang, Vera Qu, Caressa Hui, Navjot Sandhu, Maria G. Mendoza, Neil Panjwani, Yu-Cheng Chang, Chih-Hung Liang, Jen-Tang Lu, Lei Wang, Nataliya Kovalchuk, Michael F. Gensheimer, Scott G. Soltys and Erqi L. Pollom
    Citation: Radiation Oncology 2023 18:61
  8. Clear cell Renal cell carcinoma (ccRCC) is an immunogenic tumor. B7 family members, such as CTLA-4, PD-1, and PD-L1, are the main components of immune checkpoints that regulate various immune responses. Specif...

    Authors: Jung Hee Lee, Yong Jun Kim, Hyun Woo Ryu, Seung Won Shin, Eun Ji Kim, So Hyun Shin, Joon Young Park, So Young Kim, Chung Su Hwang, Joo-Young Na, Dong Hoon Shin, Jee Yeon Kim and Hyun Jung Lee
    Citation: Diagnostic Pathology 2023 18:36

    The Correction to this article has been published in Diagnostic Pathology 2023 18:63

  9. This study inventively combines epidermal growth factor receptor (EGFR) expression of the primary lesion and standardized uptake value (SUV) of positron emission tomography and computed tomography (PET/CT) to ...

    Authors: Zhaodong Fei, Ting Xu, Huiling Hong, Yiying Xu, Jiawei Chen, Xiufang Qiu, Jianming Ding, Chaoxiong Huang, Li Li, Jing Liu and Chuanben Chen
    Citation: Radiation Oncology 2023 18:33
  10. Gastroblastoma is a rare gastric tumor composed of epithelial and spindle cell components. The characteristic MALAT–GLI1 fusion gene has only been identified in 5 reported cases. We report the morphological chara...

    Authors: Ryo Sugimoto, Noriyuki Uesugi, Noriyuki Yamada, Mitsumasa Osakabe, Shigeaki Baba, Naoki Yanagawa, Yuji Akiyama, Wataru Habano, Akira Sasaki, Yoshinao Oda and Tamotsu Sugai
    Citation: Diagnostic Pathology 2023 18:24
  11. The purpose of this study was to explore whether incorporating the peritumoral region to train deep neural networks could improve the performance of the models for predicting the prognosis of NPC.

    Authors: Song Li, Xia Wan, Yu-Qin Deng, Hong-Li Hua, Sheng-Lan Li, Xi-Xiang Chen, Man-Li Zeng, Yunfei Zha and Ze-Zhang Tao
    Citation: Cancer Imaging 2023 23:14
  12. Multiparametric imaging has been seen as a route to improved prediction of chemoradiotherapy treatment outcomes. Four-dimensional volumetric perfusion CT (4D PCT) is useful for whole-organ perfusion measuremen...

    Authors: Hirokazu Tsuchiya, Munetaka Matoba, Yuka Nishino, Kiyotaka Ota, Mariko Doai, Hiroji Nagata and Hiroyuki Tuji
    Citation: Radiation Oncology 2023 18:24
  13. Lennert lymphoma (LeL) is a rare variant of peripheral T-cell lymphoma, not otherwise specified (PTCL/NOS) that is rich in epithelioid histiocytes. LeL may pose great diagnostic and therapeutic challenges to t...

    Authors: Ying Yin, Huaipu Liu, Minghua Luo, Guangyin Yu, Weihua Yin and Ping Li
    Citation: Diagnostic Pathology 2023 18:12
  14. To construct machine learning models for predicting progression free survival (PFS) and overall survival (OS) with esophageal squamous cell carcinoma (ESCC) patients.

    Authors: Yongbin Cui, Zhengjiang Li, Mingyue Xiang, Dali Han, Yong Yin and Changsheng Ma
    Citation: Radiation Oncology 2022 17:212
  15. Esophagectomy is the standard adjuvant treatment for superficial esophageal squamous cell carcinoma (SESCC) following noncurative endoscopic submucosal dissection (ESD). However, recent reports have also shown...

    Authors: Gen Suzuki, Hideya Yamazaki, Norihiro Aibe, Koji Masui, Takuya Kimoto, Shinsuke Nagasawa, Shou Watanabe, Shou Seri, Akito Asato, Atsushi Shiozaki, Hitoshi Fujiwara, Hirotaka Konishi, Osamu Dohi, Takeshi Ishikawa, Hany Elsaleh and Kei Yamada
    Citation: Radiation Oncology 2022 17:191
  16. This study was designed to establish radiation pneumonitis (RP) prediction models using dosiomics and/or deep learning-based radiomics (DLR) features based on 3D dose distribution.

    Authors: Ying Huang, Aihui Feng, Yang Lin, Hengle Gu, Hua Chen, Hao Wang, Yan Shao, Yanhua Duan, Weihai Zhuo and Zhiyong Xu
    Citation: Radiation Oncology 2022 17:188
  17. Definitive concurrent chemoradiotherapy (CCRT) is the standard treatment for locally advanced non-small cell lung cancer (LANSCLC) patients, but the treatment response and survival outcomes varied among these ...

    Authors: Nai-Bin Chen, Mai Xiong, Rui Zhou, Yin Zhou, Bo Qiu, Yi-Feng Luo, Su Zhou, Chu Chu, Qi-Wen Li, Bin Wang, Hai-Hang Jiang, Jin-Yu Guo, Kang-Qiang Peng, Chuan-Miao Xie and Hui Liu
    Citation: Radiation Oncology 2022 17:184
  18. Vulvar cancer is a rare disease, accounting for approximately 5% of gynecological malignancies. Primary adenocarcinoma of intestinal-type of the vulva or its precancerous lesion is extremely rare, and details ...

    Authors: Hanako Sato, Kosuke Murakami, Tomoyuki Otani and Noriomi Matsumura
    Citation: Diagnostic Pathology 2022 17:85
  19. To save time and have more consistent contours, fully automatic segmentation of targets and organs at risk (OAR) is a valuable asset in radiotherapy. Though current deep learning (DL) based models are on par w...

    Authors: Robert Poel, Elias Rüfenacht, Ekin Ermis, Michael Müller, Michael K. Fix, Daniel M. Aebersold, Peter Manser and Mauricio Reyes
    Citation: Radiation Oncology 2022 17:170
  20. Thymofibrolipoma has been described as a variant of thymolipoma. To date, 3 cases have been reported, and the lesion have been described to consist of extensive areas of collagenous tissue interspersed with is...

    Authors: Ryu Jokoji and Emiko Tomita
    Citation: Diagnostic Pathology 2022 17:77
  21. In nasal or sinonasal tumors, orbital invasion beyond periorbita by the tumor is one of the important criteria in the selection of the surgical procedure. We investigated the usefulness of the convolutional ne...

    Authors: Junichi Nakagawa, Noriyuki Fujima, Kenji Hirata, Minghui Tang, Satonori Tsuneta, Jun Suzuki, Taisuke Harada, Yohei Ikebe, Akihiro Homma, Satoshi Kano, Kazuyuki Minowa and Kohsuke Kudo
    Citation: Cancer Imaging 2022 22:52
  22. Fast and accurate outlining of the organs at risk (OARs) and high-risk clinical tumor volume (HRCTV) is especially important in high-dose-rate brachytherapy due to the highly time-intensive online treatment pl...

    Authors: Zhen Li, Qingyuan Zhu, Lihua Zhang, Xiaojing Yang, Zhaobin Li and Jie Fu
    Citation: Radiation Oncology 2022 17:152
  23. JCOG1015A1 is an ancillary research study to determine the organ-specific dose constraints in head and neck carcinoma treated with intensity-modulated radiation therapy (IMRT) using data from JCOG1015.

    Authors: Masahiro Inada, Yasumasa Nishimura, Satoshi Ishikura, Kazuki Ishikawa, Naoya Murakami, Takeshi Kodaira, Yoshinori Ito, Kazuhiko Tsuchiya, Yuji Murakami, Junichi Saito, Tetsuo Akimoto, Kensei Nakata, Michio Yoshimura, Teruki Teshima, Takashi Toshiyasu, Yosuke Ota…
    Citation: Radiation Oncology 2022 17:133
  24. To evaluate the clinical outcomes of hypofractionated stereotactic radiotherapy (HFSRT) combined with whole brain radiotherapy (WBRT) in patients with brain metastases (BMs).

    Authors: Xue-Yi Xie, Hong-Hua Peng, Xi Zhang, Yu-Liang Pan, Zhen Zhang and Pei-Guo Cao
    Citation: Radiation Oncology 2022 17:132
  25. Delineation of organs at risk (OAR) for anal cancer radiation therapy treatment planning is a manual and time-consuming process. Deep learning-based methods can accelerate and partially automate this task. The...

    Authors: Michael Lempart, Martin P. Nilsson, Jonas Scherman, Christian Jamtheim Gustafsson, Mikael Nilsson, Sara Alkner, Jens Engleson, Gabriel Adrian, Per Munck af Rosenschöld and Lars E. Olsson
    Citation: Radiation Oncology 2022 17:114
  26. To validate tumor volume-based imaging markers for predicting local recurrence-free survival (LRFS) in locoregionally advanced nasopharyngeal carcinoma patients, who underwent induction chemotherapy followed b...

    Authors: Ge Yan, Yan Feng, Mingyao Wu, Chao Li, Yiran Wei, Li Hua, Guoqi Zhao, Zhekai Hu, Shengyu Yao, Lingtong Hou, Xuming Chen, Qianqian Liu and Qian Huang
    Citation: Radiation Oncology 2022 17:111
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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