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      Technological advancements in cancer diagnostics: Improvements and limitations

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          Abstract

          Background

          Cancer is characterized by the rampant proliferation, growth, and infiltration of malignantly transformed cancer cells past their normal boundaries into adjacent tissues. It is the leading cause of death worldwide, responsible for approximately 19.3 million new diagnoses and 10 million deaths globally in 2020. In the United States alone, the estimated number of new diagnoses and deaths is 1.9 million and 609 360, respectively. Implementation of currently existing cancer diagnostic techniques such as positron emission tomography (PET), X‐ray computed tomography (CT), and magnetic resonance spectroscopy (MRS), and molecular diagnostic techniques, have enabled early detection rates and are instrumental not only for the therapeutic management of cancer patients, but also for early detection of the cancer itself. The effectiveness of these cancer screening programs are heavily dependent on the rate of accurate precursor lesion identification; an increased rate of identification allows for earlier onset treatment, thus decreasing the incidence of invasive cancer in the long‐term, and improving the overall prognosis. Although these diagnostic techniques are advantageous due to lack of invasiveness and easier accessibility within the clinical setting, several limitations such as optimal target definition, high signal to background ratio and associated artifacts hinder the accurate diagnosis of specific types of deep‐seated tumors, besides associated high cost. In this review we discuss various imaging, molecular, and low‐cost diagnostic tools and related technological advancements, to provide a better understanding of cancer diagnostics, unraveling new opportunities for effective management of cancer, particularly in low‐ and middle‐income countries (LMICs).

          Recent Findings

          Herein we discuss various technological advancements that are being utilized to construct an assortment of new diagnostic techniques that incorporate hardware, image reconstruction software, imaging devices, biomarkers, and even artificial intelligence algorithms, thereby providing a reliable diagnosis and analysis of the tumor. Also, we provide a brief account of alternative low cost‐effective cancer therapy devices (CryoPop®, LumaGEM®, MarginProbe®) and picture archiving and communication systems (PACS), emphasizing the need for multi‐disciplinary collaboration among radiologists, pathologists, and other involved specialties for improving cancer diagnostics.

          Conclusion

          Revolutionary technological advancements in cancer imaging and molecular biology techniques are indispensable for the accurate diagnosis and prognosis of cancer.

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          Most cited references111

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            The emerging role of lncRNAs in cancer.

            It is increasingly evident that many of the genomic mutations in cancer reside inside regions that do not encode proteins. However, these regions are often transcribed into long noncoding RNAs (lncRNAs). The recent application of next-generation sequencing to a growing number of cancer transcriptomes has indeed revealed thousands of lncRNAs whose aberrant expression is associated with different cancer types. Among the few that have been functionally characterized, several have been linked to malignant transformation. Notably, these lncRNAs have key roles in gene regulation and thus affect various aspects of cellular homeostasis, including proliferation, survival, migration or genomic stability. This review aims to summarize current knowledge of lncRNAs from the cancer perspective. It discusses the strategies that led to the identification of cancer-related lncRNAs and the methodologies and challenges involving the study of these molecules, as well as the imminent applications of these findings to the clinic.
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              Artificial intelligence in cancer imaging: Clinical challenges and applications

              Abstract Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.
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                Author and article information

                Contributors
                vermaanizer@gmail.com
                dr.papineni@connect.hku.hk , drpapineni@pactandhealth.com
                Journal
                Cancer Rep (Hoboken)
                Cancer Rep (Hoboken)
                10.1002/(ISSN)2573-8348
                CNR2
                Cancer Reports
                John Wiley and Sons Inc. (Hoboken )
                2573-8348
                06 January 2023
                February 2023
                : 6
                : 2 ( doiID: 10.1002/cnr2.v6.2 )
                : e1764
                Affiliations
                [ 1 ] University of Missouri‐Kansas City Kansas City Missouri USA
                [ 2 ] Central Research Facility Sri Ramachandra Institute of Higher Education and Research Porur Chennai India
                [ 3 ] Department of Biotechnology Indian Academy Degree College Bangalore India
                [ 4 ] PACT & Health LLC Branford Connecticut USA
                [ 5 ] Department of Surgery University of Kansas Medical Center Kansas City Kansas USA
                Author notes
                [*] [* ] Correspondence

                Rao V. L. Papineni, Department of Surgery, University of Kansas Medical Center, Kansas City, KS, USA.

                Email: dr.papineni@ 123456connect.hku.hk ; drpapineni@ 123456pactandhealth.com

                Amit Verma, PACT & Health LLC, Branford, CT, USA.

                Email: vermaanizer@ 123456gmail.com

                Author information
                https://orcid.org/0000-0001-6988-2601
                https://orcid.org/0000-0002-9138-0330
                Article
                CNR21764
                10.1002/cnr2.1764
                9940009
                36607830
                af12d344-7467-4f0f-aa28-ff8ff38bbb61
                © 2023 The Authors. Cancer Reports published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 October 2022
                : 05 October 2021
                : 27 November 2022
                Page count
                Figures: 4, Tables: 1, Pages: 17, Words: 14312
                Funding
                Funded by: NCI‐SBIR
                Award ID: 75N91019C000016
                Award ID: 75N91019C000043
                Categories
                Review
                Review
                Custom metadata
                2.0
                February 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.5 mode:remove_FC converted:20.02.2023

                2‐fluoro‐2‐deoxy‐d‐glucose,artificial intelligence,computed tomography,magnetic resonance imaging,positron emission tomography

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