Vol 5, No 3 (2024)

BACKGROUND: No publications in the Russian medical literature have examined the potential of positron emission tomography combined with computed tomography through a comparative evaluation of the effect of various targeted therapies involving tyrosine kinase inhibitors in patients with non-small cell lung cancer and mutations in the EGFR gene.

AIM: To explore the capabilities of positron emission tomography combined with computed tomography based on the RECIST 1.1 criteria and changes in SUV_max and SUV_mean metabolic parameters for the comparative assessment of the tumor response to targeted monotherapy and combination therapy with tyrosine kinase inhibitors in patients with EGFR-mutant non-small cell lung cancer.

MATERIALS AND METHODS: The 2019–2022 examination records of positron emission tomography combined with computed tomography with ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) in 105 patients with non-small cell lung cancer were analyzed, including 75 patients with EGFR-activating mutation. The radiation exposure was adjusted individually and ranged from 45 to 90 mSv. The volume activity of the ¹⁸F-FDG radiopharmaceutical was 260–500 MBq. The change in the total largest diameters of the target lesions and SUV_max and SUV_mean metabolic parameters were assessed before treatment initiation and 1.5–2.0 months after it. The follow-up duration for the changes in positron emission tomography combined with computed tomography findings in 17 patients with non-small cell lung cancer was at least 12 months.

RESULTS: According to positron emission tomography combined with computed tomography images and SUV_max and SUV_mean changes, disease progression was significantly less common (p = 0.043 and p =0.029) in patients with EGFR-mutant non-small cell lung cancer from Groups 2 and 3 who received combination therapy with tyrosine kinase inhibitors and bevacizumab or chemotherapy than in Group 1 and control group (4.2% vs. 20.0%–21.8%). An insignificant trend (p =0.092) to a higher partial response to therapy (58.3% vs. 40.0%) was noted. Similar changes in the total largest diameters of the target lesions at the early stage of therapy appeared to be not significant (p =0.187). Within the long-term follow-up of some patients with non-small cell lung cancer, in at least 50% of cases, changes in the total largest lesion diameters are consistent with the relevant SUV_max and SUV_mean alterations found in the first control study.

CONCLUSIONS: Based on the positron emission tomography combined with computed tomography data and alterations in SUV_max and SUV_mean metabolic parameters, the early tumor response to combined therapy with tyrosine kinase inhibitors and bevacizumab or chemotherapy compared with targeted monotherapy with tyrosine kinase inhibitors or chemotherapy of the control group was characterized by a significantly lower rate of metabolic disease progression, although a similar tendency of the change in the total largest diameters of target lesions according to RECIST 1.1. was not significant. Changes in SUV_max and SUV_mean metabolic parameters at the early stage of therapy are at least 50% faster than similar changes in the total largest diameters of the target lesions, which can be used for the timely identification of patients with a high risk of further progression as determined by RECIST 1.1.

BACKGROUND: Recently, significant efforts have been undertaken to find potential noninvasive biomarkers for predicting the response of locally advanced rectal cancer to neoadjuvant chemoradiotherapy.

AIM: To assess the texture characteristics of locally advanced rectal cancer in primary T2-weighted imaging (T2-WI) as a potential predictor for the efficacy of standard neoadjuvant chemoradiotherapy and develop a prediction system for the efficacy of neoadjuvant chemoradiotherapy based on them.

MATERIALS AND METHODS: The retrospective study enrolled 82 patients with locally advanced rectal cancer who received combination treatment with neoadjuvant chemoradiotherapy. Patient data were divided into the training (n=58) and control (n=24) sets. For texture analysis, primary high-resolution T2-WI at the level of the tumor center, oriented perpendicular to the intestinal wall, was used. The texture analysis was performed by second-order statistics based on the gray-level co-occurrence matrices using MAZDA ver. 4.6 featuring the calculation of 11 texture parameters. In the training set, based on the morphological assessment of surgical specimens, significantly different texture analysis parameters were found for two groups of patients: neoadjuvant chemoradiotherapy responders (good prognosis group) and nonresponders (poor prognosis group). Accordingly, a scoring system was created for assessing the efficacy of neoadjuvant chemoradiotherapy. The system was tested on the control set, and diagnostic efficacy parameters were determined.

RESULTS: In the training set, the good and poor prognosis groups differed significantly in five texture parameters: AngScMom (p=0.021), SumofSqs (p=0.003), SumEntrp (p=0.003), Entropy (p=0.038), and SumVarnc (p=0.015), for which the cutoff points were found. These parameters were applied to create the scoring system (excluding the Entropy parameter, which had a strong direct correlation with SumEntrp and the lowest area under the curve, and SumofSqs, which had low reproducibility). The diagnostic efficiency of the scoring system for predicting the response had sensitivity, specificity, positive-predictive value, and negative- predictive value of 72%, 69%, 70%, and 71% for the training set and 80%, 64%, 62%, and 82% for the control set, respectively. The areas under the ROC curve were 0.77 and 0.72 for the training and control sets, respectively.

CONCLUSIONS: Texture analysis of the primary T2-WI of tumors in patients with locally advanced rectal cancer allows for predicting the efficacy of neoadjuvant chemoradiotherapy with moderate diagnostic efficiency. The results suggest good prospects for further research in this area.

BACKGROUND: A comprehensive approach to studying hypertrophic cardiomyopathy with diagnostic equipment and the latest scanning methods will ensure quality control and effective treatment of patients with this condition. The implementation of innovative technologies and computer calculation using next-generation scanners may become relevant and promising in studying various phenotypes of left ventricular remodeling in combination with abnormalities of the chordopapillary apparatus of the mitral valve and myocardial structure.

AIM: To examine the diagnostic capabilities of computed tomography in the preoperative examination of various hypertrophic cardiomyopathy phenotypes.

MATERIALS AND METHODS: The retrospective data analysis included 47 patients with hypertrophic cardiomyopathy (mean age, 52±7 full years) before surgical correction. computed tomography was performed using our protocol with automatic bolus tracking in the left atrium with a 90 HU threshold and biphasic contrast injection to assess the heart chambers and coronary arteries anatomy and mitral valve morphology. Moreover, to assess myocardial structure remodeling, iodine dual-energy computed tomography maps obtained with delayed contrast enhancement were analyzed. All patients with hypertrophic cardiomyopathy were classified by morphological types. The anatomy of chordopapillary apparatus was evaluated in each case.

RESULTS: This study demonstrated variability in hypertrophic cardiomyopathy phenotypes, which were conventionally divided into five morphological categories, but not restricted by them. Among the patients, 26 (55%) had diffuse septum hypertrophic cardiomyopathy, 5 (11%) had midventricular hypertrophic cardiomyopathy, 2 (4%) had midventricular obstruction and apical aneurysm, 8 (18%) had focal basal septum hypertrophic cardiomyopathy, 4 (8%) had concentric hypertrophic cardiomyopathy, and the remaining 4 (8%) had apical hypertrophic cardiomyopathy. Most patients were diagnosed with chordopapillary abnormalities of the mitral valve, categorized by papillary muscle number and position, and the ratio of chords to muscles. In 10 (21%) patients, data on the myocardial bridge of a coronary artery were obtained, whereas 3 (14%) of them had dynamic stenosis. All patients had focal iodine uptake on dual-energy computed tomography maps. An extracellular volume increase was observed in 10 out of 13 (76%) patients. As shown by dual-energy computed tomography, the mean extracellular volume of the left ventricular myocardium was 30.58% (95% confidence interval, 27–34%).

CONCLUSION: Our scanning protocols developed with computed tomography scanners of various generations enable to evaluate the specific morphological patterns of hypertrophic cardiomyopathy in a single study and provide a detailed interpretation of the geometry of cardiac valves and chambers, left ventricular function, state of the coronary bed, and structural changes of the left ventricular myocardium.

BACKGROUND: The key objective of a healthcare provider is to ensure the quality and increase the efficiency of X-ray diagnostics in both municipal and private medical facilities. A poor-quality radiologic image requires repeated procedures, which results in further reputation, time, and economic losses for the medical organization. Moreover, repeated examinations further contribute to the patient’s total radiation exposure.

AIM: To assess the contribution of poor-quality X-ray examinations to patient exposure in outpatient medical organizations with telemedicine services.

MATERIALS AND METHODS: This study aimed to evaluate poor-quality X-ray examinations recorded by radiologists at the Moscow Reference Center in 2022 by applying a specially developed quality control system for diagnostic examinations based on information technology. In addition, during X-ray examinations, the methods for finding effective doses received by patients were applied in compliance with current regulatory requirements. Statistical methods were also used.

RESULTS: In this study, 2,059 poor-quality examinations were recorded, accounting for 0.084% of the total number of examinations interpreted with telemedicine. The collective effective dose from poor-quality examinations was 3.613 man-Sv, which was <0.11% of the total collective dose of patients in outpatient medical organizations.

CONCLUSION: The implementation of a quality control system for diagnostic examinations based on information technology proved to be efficient for medical organizations rendering telemedicine services.

BACKGROUND: Osteoporosis is often diagnosed at the stage with complications, i.e., low-energy fractures. Vertebral compression fractures, which are complications of osteoporosis and predictors of subsequent fractures, are often asymptomatic. Compression fractures can be found by computed tomography performed for other indications with vertebral morphometry. Approaches to using artificial intelligence algorithms designed for diagnosing vertebral compression fractures were analyzed.

AIM: Testing artificial intelligence algorithms to conduct morphometric analysis of vertebrae on chest computed tomography scans and assess the possibility of their implementation in medical organizations of the Moscow Healthcare Department.

MATERIALS AND METHODS: To set a clinical task for artificial intelligence algorithms, basic diagnostic requirements in the area of “vertebral compression fractures (osteoporosis)” were formulated. The testing of the artificial intelligence algorithms included the following stages: self-testing, functional and calibration testing, practical evaluation, and operation testing. The first three stages of testing were performed using previously generated datasets. At practical evaluation and operation testing, artificial intelligence algorithms analyzed the data from computed tomography performed in medical organizations. The expert group of radiologists assessed the diagnostic accuracy and functional capacity of the AI algorithms at all stages. The resulting quantitative metrics of the accuracy of artificial intelligence algorithms were compared with the required target values.

RESULTS: From June 2021 to June 2022, two artificial intelligence algorithms (Nos. 1 and 2) with different methods of detecting compression fractures were tested. Both artificial intelligence algorithms successfully passed the self-testing (6 tests), functional (5 tests), and calibration (100 tests) stages. The area under the ROC curve for artificial intelligence algorithm No. 1 was 0.99 (95% CI, 0.98–1), and for artificial intelligence algorithm No. 2, it was 0.91 (95% CI, 0.85–0.96). Artificial intelligence algorithm No. 1 passed the practical evaluation stage without any significant remarks, whereas algorithm No. 2 was sent for fine-tuning. After the operation testing stage, the following accuracy metrics were obtained: the areas under the ROC curve for artificial intelligence algorithm Nos. 1 and 2 were 0.93 (95% CI, 0.89–0.96) and 0.92 (95% CI, 0.90–0.94), respectively. At all stages, both artificial intelligence algorithms demonstrated sufficient metrics for clinical validation.

CONCLUSION: Artificial intelligence algorithms for the automatic diagnosis of vertebral compression fractures have been tested, demonstrating the high quality of their operation. artificial intelligence algorithms can be applied as a supplementary tool in the medical decision support system.

BACKGROUND: Based on the latest published data, 40,137 new cases of prostate cancer were reported in Russia in 2021, ranking second after lung cancer in men.

Thus, prostate cancer is one of the most common malignant neoplasms in men. Accurate and timely detection of prostate cancer is important under the current conditions.

AIM: This systematic review aimed to assess the quality of prediction models designed to detect prostate cancer during initial presentation.

MATERIALS AND METHODS: A systematic search was performed in eLibrary.ru, PubMed, Google Scholar, Web of Science, and ResearchGate for relevant publications indexed from January 2019 to September 2023 in accordance with the PRISMA protocol. Two authors independently assessed the relevant studies for potential inclusion or exclusion.

RESULTS: This systematic review meta-analysis included 21 studies. In total, data from 3,630 patients were analyzed, of which 47% had prostate cancer and 53% had benign prostate neoplasms. The mean age of the patients was 67.1 (36–90) years. In addition, 81% of the studies were based on T2-weighted imaging, 57% on diffusion-weighted imaging, and 76% on apparent diffusion coefficient. Moreover, 43% and 33% of the studies were dedicated to transition zone and prostate peripheral zone neoplasms, respectively, and 52% of the authors examined the whole prostate gland, without dividing it into zones. The most common machine-learning algorithms applied by the investigators were as follows: multiple logistic regression (76%), support vector machine (38%), and random forest (24%). Based on the meta-analysis performed for the receiver operating characteristic-area under the curve (ROC–AUC) assessment with random-effect approach in 73 prediction models described in the publications, the final ROC–AUC was 0.793 [95% CI 0.768–0.818], I² = 86.71%, p <0.001. The most accurate prediction models were based on the T2-weighted imaging + apparent diffusion coefficients imaging protocol: 0.860 [95% CI 0.813–0.907], and models created according to the “white box” principle (0.834 [95% CI 0.806–0.861]) were more accurate than the “black box” ones (0.733 [95% CI 0.695–0.771]). The models using radiomics and clinical features were slightly more accurate than those using the radiomics parameters alone (0.869 [95% CI 0.844–0.895] vs. 0.779 [95% CI 0.751–0.807]). Model accuracy was nearly identical across transitional and/or peripheral zone studies.

CONCLUSIONS: Artificial intelligence demonstrated promising results. However, the clinical applicability may require more intensive expert inspection in healthcare institutions and evaluation of efficacy in prospective studies.

Nowadays, computed tomography urography and multiparametric magnetic resonance are the most often used imaging techniques in patients with bladder cancer; however, dual-energy computed tomography is making its way into the oncological field. This narrative review article aimed to show the fundamentals of dual-energy computed tomography by outlining its physical principles, techniques, protocols, and postprocessing images to help those who used this cutting-edge technology as the first approach to fully understand its emerging applications in the evaluation of bladder cancer, a field yet to be explored. In particular, we discuss the usefulness of dual-energy computed tomography by focusing on the main images obtained, such as the iodine map, comparing them to the images obtained with conventional computed tomography scans.

Dual-energy computed tomography applications may be beneficial for bladder cancer diagnosis, staging, and treatment planning. Nevertheless, its application is limited to its availability in healthcare structures and the training of healthcare personnel who can perform and interpret the scans correctly.

In the last few years, machine learning techniques have been attracting even greater attention in the field of diagnostics, particularly when detecting breast cancer. Relevant studies dedicated to machine learning techniques in breast cancer diagnosis were analyzed in three areas: solving secondary problems that occur in modern-day breast cancer diagnostics, role in an intelligent assessment of the patient’s condition for preliminary diagnostic decisions, and capability to detect breast cancer risk factors. The results revealed that machine learning techniques applied in breast cancer diagnosis have great potential for improving diagnostic accuracy and efficiency and solving secondary problems. The medical literature analysis has determined the parameters that are used as input data in machine learning techniques. Furthermore, the collected information will be applied to create a parameter system for breast cancer diagnosis using machine learning techniques.

The severity of damage to different brain areas, including the cortex, can vary significantly in the associated neurologic deficit and reduction in the quality of life, often regardless of the lesion volume. The localization of the abnormalities plays a large part. Lesions of the dominant and subdominant hemispheres can differ greatly in both clinical features and effects on the patient’s quality of life. In this case report, a patient admitted for rehabilitation after two ischemic strokes underwent neurological and neuropsychological examination, complex instrumental diagnostics using electroencephalography, magnetic resonance imaging, computed tomography perfusion, magnetic resonance tractography, and functional magnetic resonance imaging. The patient had minimal left-sided hemiparesis, impaired regulation of voluntary activity, mild decrease in neurodynamic indicators, mildly impaired concentration, and a critical view of his condition. Neuroimaging findings demonstrated extensive postinfarction damage to the right subdominant hemisphere of the brain in the middle cerebral artery circulation. A nonconformity between the brain damage volume and the severity of its clinical signs was observed. Based on functional examination data, the dominant hemisphere was determined, and restructuring the functional centers was suggested. This clinical case was compared with similar ones, and their relationship with the data was analyzed. Information that expands the knowledge of the topography of the altered zones involved in motor and speech functions and the ability to perform arithmetic counting was obtained.

Rare complications of acute epididymitis include ischemia and infarction of the testicles. Both clinically and radiologically, it is challenging to distinguish testicular torsion. In this article we have tried to expand the library of digital images of radiological diagnostic methods used for fast and accurate differential diagnostics. This case emphasizes the significance of a comprehensive radiological assessment and how a multidisciplinary approach is necessary to guarantee an accurate diagnosis. A 24-year-old man experienced severe left testicular pain and came to the hospital 2 weeks later. At the radiology department, he reported that he had for some time painful ejaculations, pain during intercourse (dyspareunia), scrotal redness/swelling, genital inflammation, chills, swollen inguinal lymph nodes, dysuria, and scrotal pain. All diagnostic procedures were performed, first ultrasonography and then magnetic resonance imaging, as required by the urologist. The imaging studies revealed left testicular ischemia, and based on the referred clinical history, a chronic orchid-epididymitis was suspected. Thus, the condition was resolved, not with a left orchidectomy but with medical therapy because the ischemia area was not too large. The patient also had a left varicocele. Images acquired with different magnetic resonance imaging sequences were carefully examined. A rare instance of epididymal orchitis is described as a potentially dangerous complication of epididymitis and must be considered if sudden, severe scrotal pain is experienced to avoid severe consequences. This case can help with optimal patient management and prevent unnecessary interventions.

Primary extracerebral tumors are represented by benign and malignant neoplasms of the meninges and cranial nerves. Their presurgical differential diagnosis is based on the analysis of magnetic resonance imaging semiotics. The critically significant aspects for classifying tumors of this group include the following: neoplasm structure, contrast enhancement type, delimiting from the brain tissue, and relationship with the meninges or cranial nerves. Differential diagnosis of extracerebral tumors based on visual analysis of magnetic resonance imaging data is generally not challenging because most tumors have typical magnetic resonance imaging semiotics. However, in cases with atypical magnetic resonance imaging signs, reliable differentiation of tumors can be challenging. Moreover, the greatest complexity is the differentiation of meningioma grades, distinction between solitary fibrous tumors and meningiomas, and identification of the tumor type when localized in the cerebellopontine angles. The case series presented the most typical features leading to errors in the differential diagnosis of primary extracerebral tumors. All the presented tumors were verified with postsurgical histological examination. The analysis of the case reports demonstrates that a review of the combined semiotic signs can lower the number of diagnostic errors.