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Aim: Although most patients with COVID-19 experience respiratory tract infections, severe reactions to the virus may cause coagulation abnormalities that mimic other systemic coagulopathies associated with severe infections, such as disseminated intravascular coagulation and thrombotic microangiopathy. Fluctuations in platelet markers, which are an indicator of the acute phase response for COVID-19, are of clinical importance. The aim of this study is to evaluate the relationship between disease severity and Platelet Mass Index (MPI) parameters in COVID-19 patients. Material(s) and Method(s): This retrospective observational study was conducted with patients who were diagnosed with COVID-19 in a tertiary hospital. The study was continued with the remaining 280 patients. All laboratory data were scanned retrospectively from patient files and hospital information system. Result(s): A very high positive correlation was found between PMI and PLT. The PMI value in women was significantly higher than in men. It was observed that PMI did not differ significantly in terms of mortality, intubation, CPAP and comorbidity. PMI vs. Pneumonia Ct Severity Score, biochemistry parameters (AST, CRP), hemogram parameters (WBC, HGB, HCT, MCV, LYM, MPV EO) and coagulation factors (aPTT and FIB) at various levels of positive/negative, weak and strong, and significant relationship was found. There was no significant relationship between hormone and D-dimer when compared with PMI. Discussion(s): Although platelet count alone does not provide information about the prognosis of the disease, PMI may guide the clinician as an indicator of lung damage in seriously ill patients.Copyright © 2022, Derman Medical Publishing. All rights reserved.
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We retrospectively report a case of rapid exchange of a percutaneous radiologic gastrostomy tube (balloon-occluded type catheter) via off-label use of a pigtail catheter for nutrition supply during a very early episode of coronavirus disease 2019 (COVID-19) in an outpatient clinic. This case demonstrates that minimally invasive percutaneous procedures might be provided safely and effectively under appropriate precautions for preventing COVID-19 transmission during the pandemic.Copyright © 2023, Society of Gastrointestinal Intervention.
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Background: Easy availability, low cost, and low radiation exposure make chest radiography an ideal modality for coronavirus disease 2019 (COVID-19) detection. Objective(s): In this study, we propose the use of an artificial intelligence (AI) algorithm to automatically detect abnormalities associated with COVID-19 on chest radiographs. We aimed to evaluate the performance of the algorithm against the interpretation of radiologists to assess its utility as a COVID-19 triage tool. Material(s) and Method(s): The study was conducted in collaboration with Kaushalya Medical Trust Foundation Hospital, Thane, Maharashtra, between July and August 2020. We used a collection of public and private datasets to train our AI models. Specificity and sensitivity measures were used to assess the performance of the AI algorithm by comparing AI and radiology predictions using the result of the reverse transcriptase-polymerase chain reaction as reference. We also compared the existing open-source AI algorithms with our method using our private dataset to ascertain the reliability of our algorithm. Result(s): We evaluated 611 scans for semantic and non-semantic features. Our algorithm showed a sensitivity of 77.7% and a specificity of 75.4%. Our AI algorithm performed better than the radiologists who showed a sensitivity of 75.9% and specificity of 75.4%. The open-source model on the same dataset showed a large disparity in performance measures with a specificity of 46.5% and sensitivity of 91.8%, thus confirming the reliability of our approach. Conclusion(s): Our AI algorithm can aid radiologists in confirming the findings of COVID-19 pneumonia on chest radiography and identifying additional abnormalities and can be used as an assistive and complementary first-line COVID-19 triage tool.Copyright © Cancer Research, Statistics, and Treatment.
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Introduction: Chest X-rays are an important tool in COVID-19 disease management and progression.1 Several radiology societies have developed structured reporting templates to reduce interpretation variability and measure concordance.2 This study aimed to measure concordance of three international chest X-ray reporting templates in a Sydney hospital. Method(s): 12 radiologists viewed a test set of 50 COVID-19-positive patients' chest X-rays (30 classic appearance, 20 indeterminate) and 20 normal or 'other' diagnoses chest X-rays. Radiologists classified the cases according to the Royal Australian and New Zealand College of Radiology (RANZCR), British Society of Thoracic Imaging (BSTI) and modified Co-RADS (Dutch)3 templates. Intra-reader and inter-reader reliability were calculated plus measures of experiences of using templates. Result(s): Inter-reader agreement between radiologists was highest for the BSTI template (0.46), followed by RANZCR (0.36) and modified Co-RADS (0.31). The intra-reader agreement across the three templates for 'classic/characteristic' COVID-19 cases was 0.61, for 'normal' cases 0.76 and 'alternative' 0.68 with large variations that were not related to experience. Radiologists agreed the templates were easy to use and would consider using them in the future, although some cases had very low concordance (intra- and inter-reader). Conclusion(s): The BSTI template yielded highest agreement for reporting all chest X-ray types. There was a large range of intra-reader agreement for all four types of patient presentations. Further investigation of radiology lexicon is required to seek reasons for variation as well as understanding the perception of utility by referring physicians. Extension of this work should include radiographers using the templates.
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Ultrasound-guided fine-needle aspiration cytology (FNAC) is a commonly performed procedure and often the first line of diagnostic testing for patients presenting with a head and neck swelling. This technique yields a high accuracy rate and is recommended by NICE guidance. The head and neck ultrasound waiting list, consequently, has always highlighted capacity issues and this became more pronounced during Covid-19 due to the temporary cancellation of clinics. The aim of training a sonographer was to reduce the ultrasound waiting list and allow the radiologists more time in other areas, such as reporting cross-sectional imaging. The aim of this study was to document how training was undertaken, and whether FNAC success rates were comparable to those performed by radiologists. In-house training was undertaken over a 12-month period by three consultants in an acute and outpatient setting. A retrospective audit was performed of FNAC outcomes, comparing sonographer and radiologist non-diagnostic rates over an 18-month period. Statistics of the ultrasound waiting list were also analysed over this period. 250 FNAs performed by a sonographer were analysed. Results showed a 71% conclusive rate. This was compared to a previous 4-year audit, undertaken by radiologists within the department. The comparison study analysed 1222 FNAC samples and demonstrated a non-diagnostic sample of 27.2%. This was compared with the RCR live audit, which expects a 70% diagnostic rate for FNAC samples of the thyroid. This study demonstrated comparable FNAC results between a sonographer and consultant radiologist. Statistics also showed a decrease in the ultrasound waiting list, from 310 patients to 114 patients in the past 18 months. It is possible to train a sonographer to become proficient in head and neck scanning with FNAC and for cytology rates to be comparable to that of a radiologist. The study showed a positive impact on the ultrasound waiting list.
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The aim of this study was to investigate factors influencing UK sonographers' practice of adult bowel ultrasound. A mixed-method online questionnaire was designed and shared on social media platforms in April 2021. Research restrictions due to COVID19 limited the sample size permitted. Convenience sampling recruited thirty UK sonographers performing adult abdominal ultrasound in their practice. Quantitative data were analysed using descriptive statistics, and qualitative data were analysed using inductive thematic analysis. Quantitative data revealed that 53% (n= 16) of the participants expressed a lack of confidence in scanning the bowel, while 77%, (n = 23) indicated a high level of interest in training in bowel ultrasound. Although 63.3% (n = 19) of the participants reported a high level of confidence in scanning the bowel for suspected appendicitis, the majority (70%, n = 21) expressed lack of confidence in examining the bowel for other pathologies like inflammatory bowel disease (IBD). Inductive thematic analysis of qualitative data revealed that the participants had varying opinions on this topic. Emerging themes included training opportunities, preference of other imaging modalities, management challenges, sonographers, and radiologists' influence. Qualitative results suggested that factors influencing sonographer evaluation of the bowel include advanced levels of training, a high degree of support from radiologists, regular bowel ultrasound lists, audits, and feedback from clinicians. Based on the findings of this study, most sonographers are not confident in practising bowel ultrasound beyond the evaluation of suspected appendicitis. Surveyed sonographers were interested in expanding their roles into other areas of bowel ultrasound like examining for Crohn's and ulcerative colitis. Sonographer role extension into this area of practice is limited by various factors like chronic shortage of sonographers, increasing workload, limited training, and the perception of diminishing support from radiologists. We recommend a future study that is not limited by a small sample size.
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A SARS-CoV-2 virus-specific reverse transcriptase-polymerase chain reaction (RT-PCR) test is usually used to diagnose COVID-19. However, this test requires up to 2 days for completion. Moreover, to avoid false-negative outcomes, serial testing may be essential. The availability of RT-PCR test kits is currently limited, highlighting the need for alternative approaches for the precise and rapid diagnosis of COVID-19. Patients suspected to be infected with SARS-CoV-2 can be assessed using chest CT scan images. However, CT images alone cannot be used for ruling out SARS-CoV-2 infection because individual patients may exhibit normal radiological results in the primary phases of the disease. A machine learning (ML)-based recognition and segmentation system was developed to spontaneously discover and compute infection areas in CT scans of COVID-19 patients. The computable assessment exhibited suitable performance for automatic infection region allocation. The ML models developed were suitable for the direct detection of COVID-19 (+). ML was confirmed to be a complementary diagnostic technique for diagnosing COVID-19(+) by forefront medical specialists. The complete manual delineation of COVID-19 often requires up to 225.5 min; however, the proposed RILML method decreases the delineation time to 7 min after four iterations of model updating.
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Background: Coronavirus disease 2019 (COVID-19) may result in a severe acute respiratory syndrome that leads to a worldwide pandemic. Despite the increasing understanding of COVID-19 disease, the mortality rate of hospitalized COVID-19 patients remains high. Objective(s): To investigate the risk factors related to the mortality of admitted COVID-19 patients during the peak of the epidemic from August 2021 to October 2021 in Vietnam. Method(s): This is a prospective cohort study performed at the Hospital for Rehabilitation-Professional diseases. The baseline and demographic data, medical history, clinical examination, the laboratory results were recorded for patients admitted to the hospital with confirmed COVID-19. A radiologist and a pulmonologist will read the chest radiographs on admission and calculate the Brixia scores to classify the severity of lung abnormalities. Patients were followed up until beingrecovered or their death. Comparison of clinical and subclinical characteristics between recovery and death groups to find out risk factors related to the death of COVID-19 patients Results: Among 104 admitted COVID-19 patients, men accounted for 42.3%, average age of 61.7 +/- 13.7. The most common symptoms were fever 76.9%, breathlessness 74%, and fatigue 53.8%. The majority (84.6%) of the study population had at least one co-morbidity, including hypertension (53.8%), diabetes (25.9%), gastritis (19.2%), ischemic heart disease (15.4) %), stroke (9.6%) and osteoarthritis (9.6%). The rate of mild and moderate COVID-19 is 13.4%, severe 32.7%, and critical 40.4%. There are 88 inpatients (84.6%) who needed respiratory support. The median hospital stay was 13 days (IQR 10-17.75 days). The rate of intubated patients with mechanical ventilation was 31.7%. The overall mortality rate was 29.8%. Risk factors related to death included Brixia scores > 9, Urea > 7 mmol/L, Ferrtin > 578 ng/ml, Failure to get vaccinated, Age > 60 years, and Low Oxygen SpO2 < 87% (BUFFALO). Conclusion(s): The main result of the study is the independent risk factors related to the death of admitted COVID-19 patients including Brixia scores > 9, Urea > 7 mmol/L, Ferrtin > 578 ng/ml, Failure to get vaccinated, Age > 60 years, and Low Oxygen SpO2 < 87% ((BUFFALO) which suggests that these COVID-19 patients should be closely followed up.Copyright © 2023 Hanha et al.
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The new coronavirus disease was declared by WHO as COVID-19 1 and the name of the virus causing this disease was defined as SARS-CoV-2 . The most common way of transmission of the virus is the close contact with infected people and respiratory droplets. Another common way of transmission is touching mouth, nose and eyes after touching surfaces contaminated with droplets shed by infected people. According to the results of the studies, the virus has a durability between 2-72 hours on different surfaces and items..Copyright © 2022, Universidade de Sao Paulo. Museu de Zoologia. All rights reserved.
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Introduction: During the initial pandemic phase, rapid diagnosis of COVID-19 pneumonia is crucial for disease prevention and management. This study aimed to compare the deep learning (DL) module (AXIAL Skymind version 1.0) and radiologists' findings in detecting COVID-19 pneumonia changes in CT-Thorax. Method(s): A cross-sectional study from March to August 2021. 10 case studies HRCT thorax i.e. 9 studies confirmed COVID-19 pneumonia and a normal study. Patient IDs were removed and labelled by research series number. Data collected from their HRCT reports were standardized including their site and type of lesions (ground glass changes, consolidation and crazy-paving patterns) which were commonly found in COVID-19 pneumonia cases. Inter-observer agreement was measured using Fleiss Kappa (95% confidence interval). The radiologist's findings compared with the results generated by the DL module, Axial Skymind version 1.0. Result(s): A total of 330 CT-scan reports by 33 trained radiologists analysed. We used 70% agreement among radiologists as significant findings. However, the DL module managed to detect and report ground glass changes only and could not identify consolidation and crazy-paving patterns. Comparing the radiologists' findings and DL modules on ground glass changes, the average percentage of agreement for the site was 72.5%, ranging from 0-100%. The severity of the ground glass changes was not detected by DL modules. Conclusion(s): There was significant differences between DL modules and radiologists' findings on HRCT Thorax of COVID-19 pneumonia. The DL module needs to be strengthened and improve its accuracy and reliability before the potential use in clinical practice.
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Objectives: We aimed to investigate the relationship between computed tomography (CT)- based cardiothoracic ratio (CTR) with mortality rates of COVID-19 patients. Method(s): Our study was a single-center retrospective analysis of 484 patients (aged >= 18) who were admitted to our hospital's emergency department. We included only laboratory-confirmed COVID-19 patients who underwent chest CT. Data of demographic information, laboratory findings, survivals, and chest CT imaging findings were recorded. The radiologist calculated CTR by dividing the greatest transverse cardiac diameter by the greatest transverse thoracic diameter on the initial chest CT. Cardiomegaly was defined if "CTR > 0.5". Result(s): Thirty (6.2%) patients were treated as outpatients, and 135/484 (%27.9) patients were treated in the intensive care unit (ICU). A total of 147 /484 (30.4%) patients died. We found a statistical association between cardiomegaly with mortality rates (p < 0.001) and ICU admission (p = 0.008). In multivariate analysis, older age was 1.07-fold (p < 0.001), cardiomegaly 1.75-fold (p = 0.015), history of cerebrovascular diseases 2.929-fold (p = 0.018), and elevated serum LDH level 1.003-fold (p = 0.011) associated with higher risks of mortality. Conclusion(s): Since the presence of cardiomegaly on chest CT is associated with a worse prognosis for COVID-19 patients, more caution should be exercised in the evaluation, follow-up, and treatment of COVID-19 patients with cardiomegaly.Copyright © 2023 by Prusa Medical Publishing.
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Background and Aims: Data mining methods are effective and well-known tools for developing predictive models and extracting useful information from various data of patients. The present study aimed to predict the severity of patients with COVID-19 by applying the rule mining method using characteristics of medical images. Method(s): This retrospective study has analyzed the radiological data from 104 COVID-19 hospitalized patients diagnosed with COVID-19 in a hospital in Iran. A data set containing 75 binary features was generated. Apriori method is utilized for association rule mining on this data set. Only rules with confidence equal to one were generated. The performance of rules is calculated by support, coverage, and lift indexes. Result(s): Ten rules were extracted with only X-ray-related features on cases referred to ICU. The Support and Coverage index of all of these rules was 0.087, and the Lift index of them was 1.58. Thirteen rules were extracted from only CT scan-related features on cases referred to ICU. The CXR_Pleural effusion feature has appeared in all the rules. The CXR_Left upper zone feature appears in 9 rules out of 10. The Support and Coverage index of all rules was 0.15, and the Lift index of all rules was 1.63. the CT_Adjacent pleura thickening feature has appeared in all rules, and the CT_Right middle lobe appeared in 9 rules out of 13. Conclusion(s): This study could reveal the application and efficacy of CXR and CT scan imaging modalities in predicting ICU admission to a major COVID-19 infection via data mining methods. The findings of this study could help data scientists, radiologists, and clinicians in the future development and implementation of these methods in similar conditions and timely and appropriately save patients from adverse disease outcomes.Copyright © 2023 The Authors. Health Science Reports published by Wiley Periodicals LLC.
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Purposes: Artificial Intelligence (AI) models are constantly developing to help clinicians in challenging tasks such as classification of images in radiological practice. The aim of this work was to compare the diagnostic performance of an AI classifier model developed in our hospital with the results obtained from the radiologists reading the CT images in discriminating different types of viral pneumonia. Material(s) and Method(s): Chest CT images of 1028 patients with positive swab for SARS-CoV-2 (n=646) and other respiratory viruses (n=382) were segmented automatically for lung extraction and Radiomic Features (RF) of first (n=18) and second (n=120) order were extracted using PyRadiomics tools. RF, together with patient age and sex, were used to develop a Multi-Layer Perceptron classifier to discriminate images of patients with COVID-19 and non-COVID-19 viral pneumonia. The model was trained with 808 CT images performing a LASSO regression (Least Absolute Shrinkage and Selection Operator), a hyper-parameter tuning and a final 4-fold cross validation. The remaining 220 CT images (n=151 COVID-19, n=69 non-COVID-19) were used as independent validation (IV) dataset. Four readers (three radiologists with >10 years of experience and one radiology resident with 3 years of experience) were recruited to blindly evaluate the IV dataset using the 5-points scale CO-RADS score. CT images with CO-RADS >=3 were considered "COVID-19". The same images were classified as "COVID-19" or "non-COVID-19" by applying the AI model with a threshold on the predicted values of 0.5. Diagnostic accuracy, specificity, sensibility and F1 score were calculated for human readers and AI model. Result(s): The AI model was trained using 24 relevant features while the Area under ROC curve values after 4-fold cross validation and its application to the IV dataset were, respectively, 0.89 and 0.85. Interreader agreement in assigning CO-RADS class, analyzed with Fleiss' kappa with ordinal weighting, was good (k=0.68;IC95% 0.63-0.72) and diagnostic performance were then averaged among readers. Diagnostic accuracy, specificity, sensibility and F1 score resulted 78.6%, 78.3%, 78.8% and 78.5% for AI model and 77.7%, 65.6%, 83.3% and 72.0% for human readers. The difference between specificity and sensitivity observed in human readers could be related to the higher rate of false positive due to the higher incidence of COVID-19 patients in comparison with other types of viral pneumonitis during the last 2 years. Conclusion(s): A model based on RF and artificial intelligence provides comparable results with human readers in terms of diagnostic performance in a classification task.Copyright © 2023 Southern Society for Clinical Investigation.
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To assess the effectiveness of remote clinical quality management of endovascular Aim care. The system of clinical quality management of medical care in myocardial infarction (MI) including the quality of remote control of endovascular care was developed and introduced into the health care system of the Moscow Region as a part of the comprehensive study in 2008-2020. The number of people under the study was 8375. The ground for assessing the effectiveness of remote clinical management in 2019-2020 was the health care system of megapolis. Based on the analysis of 2966 endovascular procedures protocols, the treatment tactics effectiveness of intraoperative decisions was studied after an emergency coronary angiography (ECA) had been performed by interventional cardiologists. The Methods system of remote clinical quality management of endovascular care included a complex of audiovisual communications, computer system processes, mentoring and the algorithm for making an intraoperative decision. The effectiveness of remote clinical quality management of endovascular care was investigated on the number of percutaneous coronary interventions (PCI) in MI, mortality of patients with MI in the Regional vascular center in 2019-2020. The T-criteria was used to assess the reliability. The material statistical processing was carried out in the Statistica 6.0 package calculating adequate statistical indicators and their reliability at p<=0.005. Ratio PCI/ECA in 2019, January-March 2020 counted up to 48.95%. In April-December 2020 it increased up to 71.6% (p<0.001). The frequency of performing Results PCI increased by 1.46 times (p<0.001). Hospital mortality from MI decreased during the following period 2019, April-December 2020 from 9.7% to 8.2% (p = 0.005). Remote clinical management based on telemedicine and mentoring process Conclusion technologies contributes to improving the quality of endovascular care in MI.Copyright © 2021 Angles. All rights reserved.
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Background and aim: A 40-year-old male was referred to our institute for the management of a percutaneous pancreatic fistula after acute pancreatitis due to SARS-COV2 infection. He developed a peripancreatic collection(PPC) which was percutaneously drained due to infection. After the resolution of PPC, a percutaneous leakage of the main pancreatic duct (MPD) was observed, so he underwent Endoscopic Retrograde ColangioPancreatography(ERCP) with biliary plus pancreatic sphincterotomy and placement of both pancreatic and biliary stent without resolution of the leak. Material(s) and Method(s): Then he was referred to our institution, where initial management included ERCP with placement of two trans-papillary pancreatic stents and the removal of percutaneous catheter, but the fistula kept to drain. Result(s): A multidisciplinary-board decided to perform a rendezvous with interventional radiology to facilitate an endoscopic ultrasound(EUS) trans-gastric drainage of the pancreatic area draining in the percutaneous fistula. Conclusion(s): The procedure included an initial ERCP with replacement of the two pancreatic stents while the radiologist places percutaneously a guidewire through the fistula to the pancreatic point of leakage into MPD. After that, EUS identified the point in which the percutaneous guidewire was getting into the MPD and a trans-gastric EUS-guided insertion of a guidewire achieved the MPD through a 19-Gauge needle. The latter guidewire crossed the percutaneous fistula and came out. At that point, a dilation up to 10 mm was performed to create a trans-gastric pancreatic fistula. The next step was to insert percutaneously a double pigtail(10 Fr) releasing the distal side into the stomach and the proximal side into the main pancreatic duct in order to stabilize the neo-fistula. Another trans-gastric plastic stent was endoscopically placed through the pancreato-gastric neo-fistula. At the end, injection of contrast dye through the percutaneous fistula showed a complete drainage into stomach. In conclusion, the procedure achieved the complete exclusion and resolution of the pancreatic-cutaneous fistula.Copyright © 2023. Editrice Gastroenterologica Italiana S.r.l.
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Background: Breast cancer screening helps in early intervention and treatment. Post COVID, there is a huge backlog of women who missed their regular screening resulting in increased workload for radiologists, delayed reporting and intervention for malignant women. Thermalytix is an AI-based tool over thermal images that generates a 5 point score called B-Score where 5 is highest suspected risk for breast cancer and 1 is the lowest risk. In this study, we propose and evaluate a multimodal imaging modality called MaThAI that combines mammography and Thermalytix for prioritization of Mammography scans using B-Score. Material(s) and Method(s): Data from two clinical studies were pooled together and a total of 583 women who took both mammography and thermal scans were included in the study. Among them, 72 women were diagnosed to be malignant using mammography, ultrasound, and/or biopsy. Sensitivity and specificity of (i) Mammography alone (as reported by experienced radiologists), (ii) Thermalytix alone (using B-Score >=3 as positive) and (iii) MaThAI (considering a scan as positive if either Mammogram interpretation or Thermalytix interpretation or both were positive) were computed. As a second experiment, we assessed the benefit of MathAI prioritized mammography scans by estimating the reporting times for detecting 95% malignant patients. Result(s): The sensitivity and specificity of mammography were 81.9% and 98.8%, respectively, assuming BIRAD 0 as negative. Assuming BIRAD 0 as positive the sensitivity and specificity were 90.3% and 86.9%, respectively. Six malignancies were found in the 67 women with inconclusive reports (BIRADS 0). When Thermalytix B-Score was considered, the sensitivity and specificity were 94.4% and 81.0%, respectively. MaThAI showed an overall sensitivity and specificity of 98.6% (CI: 95.9%-100%) and 80.6% (CI: 77.2%-84.1%), respectively. The combo modality increased sensitivity over mammography alone by 16.7%, and Thermalytix alone by 4.2%, while decreasing the specificity of mammography by 6.3%. In the second experiment, we evaluated the benefit of MaThAI in prioritizing mammography scans using Thermalytix B-Score. Assuming mammography interpretation time is 20 minutes per exam and considering the order of the interpretation to be scan date + time, a single radiologist would have released the reports of 95% of the women with malignancy in 6720 minutes. Whereas using B-Score to reorder the scans for interpreting, the same radiologist would release the reports of 95% of the women with malignancy in 3080 minutes. Conclusion(s): MaThAI is a promising multimodal tool for breast screening that enables effective and efficient adjunct usage of thermal image along with mammography. It was effective in increasing the sensitivity of mammography by 16.7% and is estimated to reduce the reporting time for malignant patients by 54%. Conflict of interest: Ownership: Yes Board of Directors: Yes Corporate-sponsored Research: YesCopyright © 2022 Elsevier Ltd. All rights reserved
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With an increasing rate of cancers in almost all age groups and advanced screening techniques leading to an early diagnosis and longer longevity of patients with cancers, it is of utmost importance that radiologists assigned with cancer imaging should be prepared to deal with specific expected and unexpected circumstances that may arise during the lifetime of these patients. Tailored integration of preventive and curative interventions with current health plans and global escalation of efforts for timely diagnosis of cancers will pave the path for a cancer-free world. The commonly encountered circumstances in the current era, complicating cancer imaging, include coronavirus disease 2019 infection, pregnancy and lactation, immunocompromised states, bone marrow transplant, and screening of cancers in the relevant population. In this article, we discuss the imaging recommendations pertaining to cancer screening and diagnosis in the aforementioned clinical circumstances.Copyright © 2023 Wolters Kluwer Medknow Publications. All rights reserved.