Topic Editors

Prof. Dr. Maria Li Lung
Department of Clinical Oncology, University of Hong Kong, Hong Kong
Dr. Josephine Ko
Department of Clinical Oncology, University of Hong Kong, Hong Kong

Real-Time Monitoring for Improving Cancer Diagnosis and Prognosis

Abstract submission deadline
23 June 2023
Manuscript submission deadline
19 October 2023
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1228

Topic Information

Dear Colleagues,

For deadly cancers, which are difficult to successfully treat after tumor metastasis, identification of useful prognostic and predictive biomarkers for informing clinicians early during treatment of the need to revise treatments for patients is expected to improve treatment outcomes. Real- time monitoring of cancers during treatment will allow early detection of minimal residual disease and the presence of tumor heterogeneity, which need to be addressed for successful long-term treatment success. Liquid biopsies allow non-invasive serial timepoint assessment of a patient’s treatment status. Detection of circulating tumor DNA and circulating tumor cells are now reported for different cancers that herald their usefulness in cancer screening and treatment decisions. Detection of key drug-targetable mutations early during the progression of the cancer can provide better alternatives for patient treatment. This Special Issue highlights recent approaches for addressing improving patient outcomes via real-time monitoring of cancer patients during therapy to identify earlier timepoints for clinical decisions on long-term treatment.

Prof. Dr. Maria Li Lung
Dr. Josephine Ko
Topic Editors

Keywords

  • cancer biomarkers
  • liquid biopsies
  • ctDNA
  • circulating tumor cell
  • real-time monitoring

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
BioMedInformatics
biomedinformatics
- - 2021 10.3 Days 1000 CHF Submit
Cancers
cancers
6.575 5.8 2009 17.4 Days 2400 CHF Submit
Current Oncology
curroncol
3.109 3.5 1994 20.5 Days 1800 CHF Submit
Diagnostics
diagnostics
3.992 2.4 2011 17.5 Days 1800 CHF Submit
Healthcare
healthcare
3.160 2.0 2013 20.4 Days 1800 CHF Submit

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Published Papers (2 papers)

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Article
A Novel, Simple, and Low-Cost Approach for Machine Learning Screening of Kidney Cancer: An Eight-Indicator Blood Test Panel with Predictive Value for Early Diagnosis
Curr. Oncol. 2022, 29(12), 9135-9149; https://doi.org/10.3390/curroncol29120715 - 24 Nov 2022
Abstract
Clear cell renal cell carcinoma (ccRCC) accounts for more than 90% of all renal cancers. The five-year survival rate of early-stage (TNM 1) ccRCC reaches 96%, while the advanced-stage (TNM 4) is only 23%. Therefore, early screening of patients with renal cancer is [...] Read more.
Clear cell renal cell carcinoma (ccRCC) accounts for more than 90% of all renal cancers. The five-year survival rate of early-stage (TNM 1) ccRCC reaches 96%, while the advanced-stage (TNM 4) is only 23%. Therefore, early screening of patients with renal cancer is essential for the treatment of renal cancer and the long-term survival of patients. In this study, blood samples of patients were collected and a pre-defined set of blood indicators were measured. A random forest (RF) model was established to predict based on each indicator in the blood, and was trained with all relevant indicators for comprehensive predictions. In our study, we found that there was a high statistical significance (p < 0.001) for all indicators of healthy individuals and early cancer patients, except for uric acid (UA). At the same time, ccRCC also presented great differences in most blood indicators between males and females. In addition, patients with ccRCC had a higher probability of developing a low ratio of albumin (ALB) to globulin (GLB) (AGR < 1.2). Eight key indicators were used to classify and predict renal cell carcinoma. The area under the receiver operating characteristic (ROC) curve (AUC) of the eight-indicator model was as high as 0.932, the sensitivity was 88.2%, and the specificity was 86.3%, which are acceptable in many applications, thus realising early screening for renal cancer by blood indicators in a simple blood-draw physical examination. Furthermore, the composite indicator prediction method described in our study can be applied to other clinical conditions or diseases, where multiple blood indicators may be key to enhancing the diagnostic potential of screening strategies. Full article
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Review
A Scoping Review to Assess Adherence to and Clinical Outcomes of Wearable Devices in the Cancer Population
Cancers 2022, 14(18), 4437; https://doi.org/10.3390/cancers14184437 - 13 Sep 2022
Abstract
The use of wearable devices (WDs) in healthcare monitoring and management has attracted increasing attention. A major problem is patients’ adherence and acceptance of WDs given that they are already experiencing a disease burden and treatment side effects. This scoping review explored the [...] Read more.
The use of wearable devices (WDs) in healthcare monitoring and management has attracted increasing attention. A major problem is patients’ adherence and acceptance of WDs given that they are already experiencing a disease burden and treatment side effects. This scoping review explored the use of wrist-worn devices in the cancer population, with a special focus on adherence and clinical outcomes. Relevant articles focusing on the use of WDs in cancer care management were retrieved from PubMed, Scopus, and Embase from 1 January 2017 to 3 March 2022. Studies were independently screened and relevant information was extracted. We identified 752 studies, of which 38 met our inclusion criteria. Studies focused on mixed, breast, colorectal, lung, gastric, urothelial, skin, liver, and blood cancers. Adherence to WDs varied from 60% to 100%. The highest adherence was reported in the 12-week studies. Most studies focused on physical activity, sleep analysis, and heart vital signs. Of the 10 studies that described patient-reported outcomes using questionnaires and personal interviews, 8 indicated a positive correlation between the patient-reported and wearable outcomes. The definitions of the outcome measures and adherence varied across the studies. A better understanding of the intervention standards in terms of the clinical outcomes could improve adherence to wearables. Full article
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