Problems in the Early Detection of Cancer
Rajabova Maftuna Rustamovna1, Abdurashidova Kamola Turgunbayevna2, Yusupov Bakhodir Karamatovich3
1Rajabova Maftuna Rustamovna, Student, Department of Computer Systems, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent, Uzbekistan.
2Abdurashidova Kamola Turgunbayevna, Associate Professor, Head of the Department of Training of Scientific and Pedagogical Personnel, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent, Uzbekistan.
3Yusupov Bakhodir Karamatovich, Professor, Head of the Information Technologies and Artificial Intelligence department Military Institute of Information and Communication Technologies and Signals, Tashkent, Uzbekistan.
Manuscript received on 02 August 2025 | First Revised Manuscript received on 28 August 2025 | Second Revised Manuscript received on 21 October 2025 | Manuscript Accepted on 15 November 2025 | Manuscript published on 30 November 2025 | PP: 50-54 | Volume-6 Issue-1 November 2025 | Retrieval Number: 100.1/ijpmh.F112605060925 | DOI: 10.54105/ijpmh.F1126.06011125
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This article examines the analysis of existing challenges in early-stage cancer detection and approaches for their resolution. Additionally, the article discusses the probability of metastasis in early cancer detection based on actual tumour volume, as determined through diagnostic examination results. Numerous studies have demonstrated that early-stage detection and timely treatment of cancer significantly improve patients’ survival outcomes. However, long-term survival indicators may not always correlate with treatment efficacy. This phenomenon is observed in certain patients as a result of slow disease progression or early diagnosis. While survival statistics appear to improve, actual mortality rates may remain unchanged. Despite decades of scientific research, only select early detection tests demonstrate proven efficacy in reducing cancer-related mortality rates. Nevertheless, such outcomes are sometimes achieved at the cost of detecting clinically insignificant tumour states in patient populations. Detection of cancer before metastasis and surgical resection of the tumour provides the opportunity for a complete cure. If the disease has metastasized, a combination of surgical intervention and systemic therapy (chemotherapy or immunotherapy) is employed; however, a complete cure may not always be achievable. Treatment efficacy is often dependent on the extent of metastatic disease burden. Therefore, cancer detection before the emergence of clinical manifestations and before the onset of metastasis represents the optimal therapeutic approach. In some instances, detection of metastasis at relatively early stages through screening may also enhance the efficacy of systemic therapy. If novel diagnostic tests cannot differentiate between biologically progressive and clinically insignificant tumours, early detection may lead to increased incidence rates without reducing cancer mortality. Detection of small tumours requires screening at short intervals; however, this increases the number of overdiagnoses, unnecessary tests, and false-positive results. In the context of biological heterogeneity in tumour growth rates, adapting the screening frequency to tumour kinetics plays a crucial role in balancing the benefit-to-harm ratio.
Keywords: Cancer, Metastasis, Screening, Liquid Biopsy, Medical Imaging, Artificial Intelligence (AI), The Tumour Volume Doubling Time (TVDT), Convolutional Neural Network (CNN), Deep Learning, Machine Learning.
Scope of the Article: Health Care Management