Keywords
data visualization
TherDep medical information system
diagnostics and treatment with radioactive iodine
How to Cite
Abstract
The incidence of differentiated thyroid cancer has been
gradually increasing worldwide over the past decades, and Ukraine
is no exception, especially after the Chornobyl accident. Currently,
this oncopathology has already become the fastest growing malignancy.
Among the various types of well differentiated thyroid carcinomas
(WDTC), the most common are papillary thyroid cancer,
which typically grows in one lobe of the gland (almost 70-80% of
all cases) and follicular thyroid cancer, which accounts for approximately
20-30% of cases. In Ukraine, the increase in WDTC occurs
mainly among women in the age of over 50 years. 2. To highlight
the effectiveness of the developed data visualization procedure
during the observation of patients with thyroid dysfunction, allowing
for a quick and easy visualization of the treatment and observation
history of these patients. Purpose of the study. 1. To develop
a visualization form that would allow (in a single window) the entire
dynamics of performed scintigraphy, TG, ATTH and TSH values, as
well as key ultrasound data to be viewed. 2. To highlight the effectiveness
of the developed data visualization procedure during the
observation of patients with (WDTC, allowing for a quick and easy
visualization of the treatment and observation history of these patients.
Material and methods. Since 1996, the V.P. Komissarenko
Institute of Endocrinology and Metabolism of the National Academy
of Medical Sciences of Ukraine has been using its own medical information
system (MIS), TherDep, which stores all the necessary information
(ultrasound and scintigraphy protocols and findings, and
the results of TSH, TG, and ATTG tests) for analyzing data on patients
with WDTC. The effectiveness of the results obtained was assessed
by measuring the time required to analyze the clinical situation in
a patient with WDTC and metastatic disease in the lungs. The time
required to analyze the situation before and after the implementation
of the developed visualization form was compared. The sample
included patients from a high-risk group group for recurrence or
persistence of the disease with lung metastases, since they have
the greatest information saturation of data for analysis. Time in individual
groups was compared using standard statistical methods of
parametric data analysis, and the statistical significance of the difference
between groups was also determined using the chi-square
method. Results. Using this visualization reduces the time spent
reviewing and analyzing data, allowing the physician to spend more
time interacting with the patient during the appointment. Furthermore,
the incidence of medical errors regarding the patient’s prescriptions
is significantly reduced, given the clear presentation of all
information dynamically and the absence of the need to remember
all previous data, which is important for the physician’s comfortable
and effective work. Conclusions. This visualization is especially recommended
for physicians working with patients under long-term
observation and who have accumulated a large amount of data
(laboratory and instrumental).
References
Adilbay D, Yuan A, Romesser PB, Wong RJ, Shah JP, Shaha AR, et al. Well-differentiated thyroid cancer: Who should get postoperative radiation? Ann Surg Oncol. 2022 Sep;29(9):5582-90. doi:10.1245/s10434-022-11898-2.
Nabhan F, Dedhia PH, Ringel MD. Thyroid cancer, recent advances in diagnosis and therapy. Int J Cancer. 2021 Sep 1;149(5):984-92. doi: 10.1002/ijc.33690.
Asa SL. The current histologic classification of thyroid cancer. Endocrinol Metab Clin North Am. 2019 Mar;48(1):1-22. doi: 10.1016/j.ecl.2018.10.001.
Howlader N, Noone AM, Krapcho M, Miller D, Brest A, Yu M, et al. (eds). SEER cancer statistics review, 1975-2017, National Cancer Institute. Bethesda, MD, https://seer.cancer.gov/csr/1975_2017/, based on November 2019 SEER data submission, posted to the SEER web site, April 2020.
Olson E, Wintheiser G, Wolfe KM, Droessler J, Silberstein PT. Epidemiology of thyroid cancer: A review of the national cancer database, 2000-2013. Review. Cureus. Feb 2019;11(2):11. UNSP e4127. doi:10.7759/cureus.4127)
Crippa F, Alessi A, Gerali A, Bombardieri E. FDG-PET in thyroid cancer. Tumori. 2003 Sep-Oct;89(5):540-3. doi: 10.1177/03008916030890051
Erratum in: Tumori. 2004 May-Jun; 90(3):366 7. Sakulpisuti C, Charoenphun P, Chamroonrat W. Positron emission tomography radiopharmaceuticals in differentiated thyroid cancer. Molecules. 2022 Aug 3;27(15):4936. doi: 10.3390/molecules27154936.
Ullmann TM, Papaleontiou M, Sosa JA. Current controversies in low-risk differentiated thyroid cancer: Reducing overtreatment in an era of overdiagnosis. J Clin Endocrinol Metab. 2023 Jan 17;108(2):271-80. doi: 10.1210/clinem/dgac646.
Park H, Park J, Park SY, Kim TH, Kim SW, Chung JH. Clinical course from diagnosis to death in patients with well-differentiated thyroid cancer. Cancers (Basel). 2020 Aug 18;12(8):2323. doi: 10.3390/cancers12082323.
Prabhu M, Shakya S, Ballal S, Ahmed Shamim S, Bal C. RET gene mutation analysis and long-term clinical outcomes of medullary thyroid cancer patients. Nucl Med Commun. 2020 Nov;41(11):1136-42. doi: 10.1097/MNM.0000000000001264.