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Early Clinical and Laboratory Diagnosis of Malignant Tumors
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Early Clinical and Laboratory Diagnosis of Malignant Tumors

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Clinical Laboratory Medicine".

Deadline for manuscript submissions: closed (25 June 2023) | Viewed by 4777

Special Issue Editor

Prof. Dr. Yaping Tian

E-Mail Website
Guest Editor
Center for Birth Defects Prevention and Control Technology Research, Department of Medical Research and Innovation, Chinese PLA General Hospital, Beijing 100853, China
Interests: laboratory diagnosis; tumors; cardiovascular diseases; prevention and control of birth defects; free radical biology

Special Issue Information

Dear Colleagues,

With the deep understanding of the pathological progression of malignant tumors in addition to the development of new technology in the field of biology, we have found more new specific biomarkers for the early finding of incidences of mutation, risks of malignance, and early tumor formation. Advanced technologies, such as gene sequencing, PCR, biochips, microfluidic systems, quantum dot labeling, etc., have been used in clinical diagnoses, which will promote the early finding of tumors. On the other hand, new precision treatments and tumor prevention methods allow tumor reversal and early therapy to be available, which also promote the development of the "Early Clinical and Laboratory Diagnosis of Malignant Tumors". In this Special Issue, we welcome authors to submit papers on new methods and technologies of, as well as knowledge on, malignant tumor early diagnosis and prevention.

Prof. Dr. Yaping Tian
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • laboratory diagnosis
  • malignant tumors
  • gene sequencing
  • biochips
  • microfluidic system
  • quantum dot labeling
  • liquid biopsy
  • digital PCR
  • immunolabeling analysis
  • tumor reversal

Published Papers (3 papers)

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Research

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15 pages, 2858 KiB  
Article
Application of Nonlinear Models Combined with Conventional Laboratory Indicators for the Diagnosis and Differential Diagnosis of Ovarian Cancer
by Tongshuo Zhang, Aibo Pang, Jungang Lyu, Hefei Ren, Jiangnan Song, Feng Zhu, Jinlong Liu, Yuntao Cui, Cunbao Ling and Yaping Tian
J. Clin. Med. 2023, 12(3), 844; https://doi.org/10.3390/jcm12030844 - 20 Jan 2023
Viewed by 1309
Abstract
Existing biomarkers for ovarian cancer lack sensitivity and specificity. We compared the diagnostic efficacy of nonlinear machine learning and linear statistical models for diagnosing ovarian cancer using a combination of conventional laboratory indicators. We divided 901 retrospective samples into an ovarian cancer group [...] Read more.
Existing biomarkers for ovarian cancer lack sensitivity and specificity. We compared the diagnostic efficacy of nonlinear machine learning and linear statistical models for diagnosing ovarian cancer using a combination of conventional laboratory indicators. We divided 901 retrospective samples into an ovarian cancer group and a control group, comprising non-ovarian malignant gynecological tumor (NOMGT), benign gynecological disease (BGD), and healthy control subgroups. Cases were randomly assigned to training and internal validation sets. Two linear (logistic regression (LR) and Fisher’s linear discriminant (FLD)) and three nonlinear models (support vector machine (SVM), random forest (RF), and artificial neural network (ANN)) were constructed using 22 conventional laboratory indicators and three demographic characteristics. Model performance was compared. In an independent prospectively recruited validation set, the order of diagnostic efficiency was RF, SVM, ANN, FLD, LR, and carbohydrate antigen 125 (CA125)-only (AUC, accuracy: 0.989, 95.6%; 0.985, 94.4%; 0.974, 93.4%; 0.915, 82.1%; 0.859, 80.1%; and 0.732, 73.0%, respectively). RF maintained satisfactory classification performance for identifying different ovarian cancer stages and for discriminating it from NOMGT-, BGD-, or CA125-positive control. Nonlinear models outperformed linear models, indicating that nonlinear machine learning models can efficiently use conventional laboratory indicators for ovarian cancer diagnosis. Full article
(This article belongs to the Special Issue Early Clinical and Laboratory Diagnosis of Malignant Tumors)
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Review

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14 pages, 1586 KiB  
Review
The Present and Future of Artificial Intelligence in Urological Cancer
by Xun Liu, Jianxi Shi, Zhaopeng Li, Yue Huang, Zhihong Zhang and Changwen Zhang
J. Clin. Med. 2023, 12(15), 4995; https://doi.org/10.3390/jcm12154995 - 29 Jul 2023
Cited by 1 | Viewed by 1379
Abstract
Artificial intelligence has drawn more and more attention for both research and application in the field of medicine. It has considerable potential for urological cancer detection, therapy, and prognosis prediction due to its ability to choose features in data to complete a particular [...] Read more.
Artificial intelligence has drawn more and more attention for both research and application in the field of medicine. It has considerable potential for urological cancer detection, therapy, and prognosis prediction due to its ability to choose features in data to complete a particular task autonomously. Although the clinical application of AI is still immature and faces drawbacks such as insufficient data and a lack of prospective clinical trials, AI will play an essential role in individualization and the whole management of cancers as research progresses. In this review, we summarize the applications and studies of AI in major urological cancers, including tumor diagnosis, treatment, and prognosis prediction. Moreover, we discuss the current challenges and future applications of AI. Full article
(This article belongs to the Special Issue Early Clinical and Laboratory Diagnosis of Malignant Tumors)
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Other

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14 pages, 2316 KiB  
Systematic Review
Distinct Clinical and Prognostic Features of Myelodysplastic Syndrome in Patients from the Middle East, North Africa, and Beyond: A Systemic Review
by Amal Al-Haidose, Mohamed A. Yassin, Muna N. Ahmed, Hasna H. Kunhipurayil, Asrar A. Al-Harbi, Musheer A. Aljaberi, Saddam A. Abbasi, Shahram Kordasti and Atiyeh M. Abdallah
J. Clin. Med. 2023, 12(8), 2832; https://doi.org/10.3390/jcm12082832 - 12 Apr 2023
Cited by 3 | Viewed by 1765
Abstract
Myelodysplastic syndrome (MDS) describes a group of bone marrow malignancies with variable morphologies and heterogeneous clinical features. The aim of this study was to systematically appraise the published clinical, laboratory, and pathologic characteristics and identify distinct clinical features of MDS in the Middle [...] Read more.
Myelodysplastic syndrome (MDS) describes a group of bone marrow malignancies with variable morphologies and heterogeneous clinical features. The aim of this study was to systematically appraise the published clinical, laboratory, and pathologic characteristics and identify distinct clinical features of MDS in the Middle East and North Africa (MENA) region. We conducted a comprehensive search of the PubMed, Web of Science, EMBASE, and Cochrane Library databases from 2000 to 2021 to identify population-based studies of MDS epidemiology in MENA countries. Of 1935 studies, 13 independent studies published between 2000 and 2021 representing 1306 patients with MDS in the MENA region were included. There was a median of 85 (range 20 to 243) patients per study. Seven studies were performed in Asian MENA countries (732 patients, 56%) and six in North African MENA countries (574 patients, 44%). The pooled mean age was 58.4 years (SD 13.14; 12 studies), and the male-to-female ratio was 1.4. The distribution of WHO MDS subtypes was significantly different between MENA, Western, and Far East populations (n = 978 patients, p < 0.001). More patients from MENA countries were at high/very high IPSS risk than in Western and Far East populations (730 patients, p < 0.001). There were 562 patients (62.2%) with normal karyotypes and 341 (37.8%) with abnormal karyotypes. Our findings establish that MDS is prevalent within the MENA region and is more severe than in Western populations. MDS appears to be more severe with an unfavorable prognosis in the Asian MENA population than the North African MENA population. Full article
(This article belongs to the Special Issue Early Clinical and Laboratory Diagnosis of Malignant Tumors)
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