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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. Accurate segmentation of gastric tumors from CT scans provides useful image information for guiding the diagnosis and treatment of gastric cancer. However, automated gastric tumor segmentation from 3D CT image...

    Authors: Ning Yuan, Yongtao Zhang, Kuan Lv, Yiyao Liu, Aocai Yang, Pianpian Hu, Hongwei Yu, Xiaowei Han, Xing Guo, Junfeng Li, Tianfu Wang, Baiying Lei and Guolin Ma
    Citation: Cancer Imaging 2024 24:63
  2. Accurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue appearance. In this paper, we developed a novel automated three-dimensi...

    Authors: Alexander F. I. Osman, Kholoud S. Al-Mugren, Nissren M. Tamam and Bilal Shahine
    Citation: Radiation Oncology 2024 19:61
  3. The brachytherapy is an indispensable treatment for gynecological tumors, but the quality and efficiency of brachytherapy training for residents is still unclear.

    Authors: Mohan Dong, Changhao Liu, Junfang Yan, Yong Zhu, Yutian Yin, Jia Wang, Ying Zhang, Lichun Wei and Lina Zhao
    Citation: Radiation Oncology 2024 19:60
  4. EBUS-TBNA has emerged as an important minimally invasive procedure for the diagnosis and staging of lung cancer. Our objective was to evaluate the effect of different specimen preparation from aspirates on the...

    Authors: Hansheng Wang, Jiankun Wang, Yan Liu, Yunyun Wang, Yanhui Zhou, Dan Yu, Hui You, Tao Ren, Yijun Tang and Meifang Wang
    Citation: Diagnostic Pathology 2024 19:61
  5. To create radiomics signatures based on habitat to assess the instant response in lung metastases of colorectal cancer (CRC) after radiofrequency ablation (RFA).

    Authors: Haozhe Huang, Hong Chen, Dezhong Zheng, Chao Chen, Ying Wang, Lichao Xu, Yaohui Wang, Xinhong He, Yuanyuan Yang and Wentao Li
    Citation: Cancer Imaging 2024 24:44
  6. Oral squamous cell carcinoma in minors is considered to be a distinct entity from OSCC in older patients, with an uncertain etiology. Human papillomavirus (HPV) infection may trigger the initiation and promote...

    Authors: Ningxiang Wu, Yonghui Li, Xiaokun Ma, Zhen Huang, Zhuoxuan Chen, Weihua Chen and Ran Zhang
    Citation: Diagnostic Pathology 2024 19:51
  7. Primary mucoepidermoid carcinomas (MECs) of the sinonasal tract and nasopharynx are rare entities that represent a diagnostic challenge, especially in biopsy samples. Herein, we present a case series of MECs o...

    Authors: Chunyan Hu, Lan Lin, Ming Ye, Yifeng Liu, Qiang Huang, Cuncun Yuan, Ji Sun and Hui Sun
    Citation: Diagnostic Pathology 2024 19:46
  8. Identifying breast cancer (BC) patients with germline breast cancer susceptibility gene (gBRCA) mutation is important. The current criteria for germline testing for BC remain controversial. This study aimed to de...

    Authors: Tingting Deng, Jianwen Liang, Cuiju Yan, Mengqian Ni, Huiling Xiang, Chunyan Li, Jinjing Ou, Qingguang Lin, Lixian Liu, Guoxue Tang, Rongzhen Luo, Xin An, Yi Gao and Xi Lin
    Citation: Cancer Imaging 2024 24:31
  9. This study aimed to present a deep-learning network called contrastive learning-based cycle generative adversarial networks (CLCGAN) to mitigate streak artifacts and correct the CT value in four-dimensional co...

    Authors: Nannan Cao, Ziyi Wang, Jiangyi Ding, Heng Zhang, Sai Zhang, Liugang Gao, Jiawei Sun, Kai Xie and Xinye Ni
    Citation: Radiation Oncology 2024 19:20
  10. Classifying and characterizing pulmonary lesions are critical for clinical decision-making process to identify optimal therapeutic strategies. The purpose of this study was to develop and validate a radiomics ...

    Authors: Jiaxuan Zhou, Yu Wen, Ruolin Ding, Jieqiong Liu, Hanzhen Fang, Xinchun Li, Kangyan Zhao and Qi Wan
    Citation: Cancer Imaging 2024 24:14
  11. This study aimed to investigate the value of clinical, radiomic features extracted from gross tumor volumes (GTVs) delineated on CT images, dose distributions (Dosiomics), and fusion of CT and dose distributio...

    Authors: Zahra Mansouri, Yazdan Salimi, Mehdi Amini, Ghasem Hajianfar, Mehrdad Oveisi, Isaac Shiri and Habib Zaidi
    Citation: Radiation Oncology 2024 19:12
  12. Stereotactic body radiotherapy (SBRT) is a treatment option for patients with early-stage non-small cell lung cancer (NSCLC) who are unfit for surgery. Some patients may experience distant metastasis. This study ...

    Authors: Lu Yu, Zhen Zhang, HeQing Yi, Jin Wang, Junyi Li, Xiaofeng Wang, Hui Bai, Hong Ge, Xiaoli Zheng, Jianjiao Ni, Haoran Qi, Yong Guan, Wengui Xu, Zhengfei Zhu, Ligang Xing, Andre Dekker…
    Citation: Radiation Oncology 2024 19:10
  13. In solid-predominantly invasive lung adenocarcinoma (SPILAC), occult lymph node metastasis (OLNM) is pivotal for determining treatment strategies. This study seeks to develop and validate a fusion model combin...

    Authors: Weiwei Tian, Qinqin Yan, Xinyu Huang, Rui Feng, Fei Shan, Daoying Geng and Zhiyong Zhang
    Citation: Cancer Imaging 2024 24:8
  14. Deep learning-based auto-segmentation of head and neck cancer (HNC) tumors is expected to have better reproducibility than manual delineation. Positron emission tomography (PET) and computed tomography (CT) ar...

    Authors: Yiling Wang, Elia Lombardo, Lili Huang, Michele Avanzo, Giuseppe Fanetti, Giovanni Franchin, Sebastian Zschaeck, Julian Weingärtner, Claus Belka, Marco Riboldi, Christopher Kurz and Guillaume Landry
    Citation: Radiation Oncology 2024 19:3
  15. NRG1 fusion is a promising therapeutic target for various tumors but its prevalence is extremely low, and there are no standardized testing algorithms for genetic assessment.

    Authors: Xiaomei Zhang, Lin Li, Fuping Gao, Binbin Liu, Jing Li, Shuang Ren, Shuangshuang Peng, Wei Qiu, Xiaohong Pu and Qing Ye
    Citation: Diagnostic Pathology 2024 19:1
  16. Artificial intelligence (AI) systems are proposed as a replacement of the first reader in double reading within mammography screening. We aimed to assess cancer detection accuracy of an AI system in a Danish s...

    Authors: Mohammad Talal Elhakim, Sarah Wordenskjold Stougaard, Ole Graumann, Mads Nielsen, Kristina Lång, Oke Gerke, Lisbet Brønsro Larsen and Benjamin Schnack Brandt Rasmussen
    Citation: Cancer Imaging 2023 23:127
  17. The study retrospectively analyzed the accuracy and predictive ability of preoperative integrated whole-body 18F-FDG PET/CT for the assessment of high-risk factors in patients with endometrial carcinoma (EC).

    Authors: Ye Yang, Yu-Qin Pan, Min Wang, Song Gu and Wei Bao
    Citation: Radiation Oncology 2023 18:196
  18. Although magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis studies based on deep learning have significantly progressed, the similarity between synthetic CT (sCT) and real CT (rCT) has onl...

    Authors: Siqi Yuan, Xinyuan Chen, Yuxiang Liu, Ji Zhu, Kuo Men and Jianrong Dai
    Citation: Radiation Oncology 2023 18:182
  19. Although neural networks have shown remarkable performance in medical image analysis, their translation into clinical practice remains difficult due to their lack of interpretability. An emerging field that ad...

    Authors: Marion Dörrich, Markus Hecht, Rainer Fietkau, Arndt Hartmann, Heinrich Iro, Antoniu-Oreste Gostian, Markus Eckstein and Andreas M. Kist
    Citation: Diagnostic Pathology 2023 18:121
  20. Accurate prediction of response to neoadjuvant chemoradiotherapy (nCRT) is very important for treatment plan decision in locally advanced rectal cancer (LARC). The aim of this study was to investigate whether ...

    Authors: Xuezhi Zhou, Yi Yu, Yanru Feng, Guojun Ding, Peng Liu, Luying Liu, Wenjie Ren, Yuan Zhu and Wuteng Cao
    Citation: Radiation Oncology 2023 18:175
  21. This study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) who received chemotherapy alone as...

    Authors: Runsheng Chang, Shouliang Qi, Yanan Wu, Yong Yue, Xiaoye Zhang and Wei Qian
    Citation: Cancer Imaging 2023 23:101
  22. Accurate delineation of clinical target volume of tumor bed (CTV-TB) is important but it is also challenging due to surgical effects and soft tissue contrast. Recently a few auto-segmentation methods were deve...

    Authors: Xin Xie, Yuchun Song, Feng Ye, Shulian Wang, Hui Yan, Xinming Zhao and Jianrong Dai
    Citation: Radiation Oncology 2023 18:170
  23. The integration of Artificial Intelligence (AI) technology in cancer care has gained unprecedented global attention over the past few decades. This has impacted the way that cancer care is practiced and delive...

    Authors: Iman Hesso, Reem Kayyali, Debbie-Rose Dolton, Kwanyoung Joo, Lithin Zacharias, Andreas Charalambous, Maria Lavdaniti, Evangelia Stalika, Tarek Ajami, Wanda Acampa, Jasmina Boban and Shereen Nabhani-Gebara
    Citation: Radiation Oncology 2023 18:167
  24. Manual clinical target volume (CTV) and gross tumor volume (GTV) delineation for rectal cancer neoadjuvant radiotherapy is pivotal but labor-intensive. This study aims to propose a deep learning (DL)-based wor...

    Authors: Jianhao Geng, Xianggao Zhu, Zhiyan Liu, Qi Chen, Lu Bai, Shaobin Wang, Yongheng Li, Hao Wu, Haizhen Yue and Yi Du
    Citation: Radiation Oncology 2023 18:164
  25. The goal of this study is to demonstrate the performance of radiomics and CNN-based classifiers in determining the primary origin of gastrointestinal liver metastases for visually indistinguishable lesions.

    Authors: Hishan Tharmaseelan, Abhinay K. Vellala, Alexander Hertel, Fabian Tollens, Lukas T. Rotkopf, Johann Rink, Piotr Woźnicki, Isabelle Ayx, Sönke Bartling, Dominik Nörenberg, Stefan O. Schoenberg and Matthias F. Froelich
    Citation: Cancer Imaging 2023 23:95
  26. Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic pathology has gone through a s...

    Authors: Saba Shafi and Anil V. Parwani
    Citation: Diagnostic Pathology 2023 18:109
  27. To explore the application of magnetic resonance imaging (MRI) in the evaluation of radiation-induced sinusitis (RIS), MRI-based scoring system was used to evaluate the development regularity, characteristics ...

    Authors: Wenya Zheng, Tao Yan, Dongjiao Liu, Geng Chen, Yingjuan Wen, Xiuli Rao, Yizhe Wang, Huijuan Zheng, Jiahong Yang and Hua Peng
    Citation: Radiation Oncology 2023 18:153
  28. Adaptive radiotherapy (ART) was introduced in the late 1990s to improve the accuracy and efficiency of therapy and minimize radiation-induced toxicities. ART combines multiple tools for imaging, assessing the ...

    Authors: Hefei Liu, David Schaal, Heather Curry, Ryan Clark, Anthony Magliari, Patrick Kupelian, Deepak Khuntia and Sushil Beriwal
    Citation: Radiation Oncology 2023 18:144
  29. Magnetic resonance imaging guided radiotherapy (MRgRT) offers treatment plan adaptation to the anatomy of the day. In the current MRgRT workflow, this requires the time consuming and repetitive task of manual ...

    Authors: Marvin F. Ribeiro, Sebastian Marschner, Maria Kawula, Moritz Rabe, Stefanie Corradini, Claus Belka, Marco Riboldi, Guillaume Landry and Christopher Kurz
    Citation: Radiation Oncology 2023 18:135
  30. To determine the predictive indexes of late cervical lymph node metastasis in early tongue squamous cell carcinoma (TSCC). We retrospectively analyzed the cases of 25 patients with stage I/II TSCC who had unde...

    Authors: Koroku Kato, Hiroki Miyazawa, Hisano Kobayashi, Yoshiaki Kishikawa, Hayato Funaki, Natsuyo Noguchi, Kazuhiro Ooi and Shuichi Kawashiri
    Citation: Diagnostic Pathology 2023 18:87
  31. Spinal metastasis and multiple myeloma share many overlapping conventional radiographic imaging characteristics, thus, their differentiation may be challenging. The purpose of this study was to develop and val...

    Authors: Shuai Zhang, Menghan Liu, Sha Li, Jingjing Cui, Guang Zhang and Ximing Wang
    Citation: Cancer Imaging 2023 23:72
  32. To build and validate a radiomics nomogram based on preoperative CT scans and clinical data for detecting synchronous ovarian metastasis (SOM) in female gastric cancer (GC) cases.

    Authors: Qian-Wen Zhang, Pan-Pan Yang, Yong-Jun-Yi Gao, Zhi-Hui Li, Yuan Yuan, Si-Jie Li, Shao-Feng Duan, Cheng-Wei Shao, Qiang Hao, Yong Lu, Qi Chen and Fu Shen
    Citation: Cancer Imaging 2023 23:71
  33. To investigate the feasibility and performance of deep learning (DL) models combined with plan complexity (PC) and dosiomics features in the patient-specific quality assurance (PSQA) for patients underwent vol...

    Authors: Ce Han, Ji Zhang, Bing Yu, Haoze Zheng, Yibo Wu, Zhixi Lin, Boda Ning, Jinling Yi, Congying Xie and Xiance Jin
    Citation: Radiation Oncology 2023 18:116
  34. This study was to improve image quality for high-speed MR imaging using a deep learning method for online adaptive radiotherapy in prostate cancer. We then evaluated its benefits on image registration.

    Authors: Ji Zhu, Xinyuan Chen, Yuxiang Liu, Bining Yang, Ran Wei, Shirui Qin, Zhuanbo Yang, Zhihui Hu, Jianrong Dai and Kuo Men
    Citation: Radiation Oncology 2023 18:108
  35. To establish a prognostic model to predict the overall survival (OS) in patients with unresectable hepatocellular carcinoma (HCC) treated with intensity modulated radiotherapy (IMRT).

    Authors: Meiying Long, Jianxu Li, Meiling He, Jialin Qiu, Ruijun Zhang, Yingchun Liu, Chunfeng Liang, Haiyan Lu, Yadan Pang, Hongmei Zhou, Hongping Yu and Moqin Qiu
    Citation: Radiation Oncology 2023 18:96
  36. Segmentation of the Gross Tumor Volume (GTV) is a crucial step in the brachytherapy (BT) treatment planning workflow. Currently, radiation oncologists segment the GTV manually, which is time-consuming. The tim...

    Authors: Roque Rodríguez Outeiral, Patrick J. González, Eva E. Schaake, Uulke A. van der Heide and Rita Simões
    Citation: Radiation Oncology 2023 18:91
  37. Low- and middle-income countries (LMICs) represent a big source of data not only for endemic diseases but also for neoplasms. Data is the fuel which drives the modern era. Data when stored in digital form can ...

    Authors: Talat Zehra, Anil Parwani, Jamshid Abdul-Ghafar and Zubair Ahmad
    Citation: Diagnostic Pathology 2023 18:68
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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