Chest Chest Computed Tomography Scan Research Articles

ESTIMATION OF FETAL AND PEDIATRIC DOSES FROM CHEST CT EXAMINATIONS USING VIRTUALDOSE SOFTWARE.

Pregnant women and children sometimes had to undergo chest computed tomography (CT) scans during the Corona Virus Disease 2019 (COVID-19) pandemic. This study estimated the fetal and pediatric doses from chest CT scans. Organ doses and effective doses were calculated using the VirtualDose-CT software. Two groups of computational human phantoms, pregnant females and pediatric patients were used in this study. The results of doses normalized to volumetric CT Dose Index (CTDIvol) can be used universally for other dosimetry studies. Based on our calculations and international survey data of CTDIvol, fetal absorbed doses from COVID-19-related chest CT were found to be 0.04-0.36, 0.05-0.44 and 0.07-0.61mGy for 3, 6 and 9months of pregnancy, respectively. When the scan range is extended to the abdominal region, fetal doses increase by almost 4-fold. Effective doses for COVID-19-related chest CT were 1.62-13.77, 1.58-13.46, 1.57-13.33 and 1.29-10.98mSv for the newborn, 1-, 5- and 10-y-old children, respectively. In addition, the effects of specific axial scan ranges exceeding the thorax region were evaluated. Although doses from chest CT scans are small, such data allow radiologists and patients to be informed of the dose levels and ways to avoid unnecessary radiation.

Should We Always Perform Preoperative Chest Computed Tomography in Patients with cT1a Renal Cell Carcinoma?

No definitive criteria regarding the performance of preoperative chest computed tomography (CT) in patients with cT1a renal cell carcinoma (RCC) exists. We aimed to establish an objective standard for the optimal timing of preoperative chest CT in patients with RCC. Data from 890 patients who underwent surgical treatment for RCC between January 2011 and December 2020 were retrospectively collected. The primary endpoint was detection of lung metastasis on chest CT before nephrectomy. A multivariable logistic regression model predicting positive chest CT scans was used. Predictors included preoperative cTN stage, presence of systemic symptoms, Charlson comorbidity index (CCI), platelet count/hemoglobin ratio, albumin/globulin ratio (AGR), and De Ritis ratio. The overall rate of positive chest CT scans before nephrectomy was 3.03% (27/890). Only one patient had lung metastasis before surgery for cT1a. cT stage (≥cT1b), CCI ≥4, and low AGR were associated with a higher risk of positive chest CT scans. The best cutoff value for AGR was 1.39. After 890-sample bootstrap validation, the concordance index was 0.80. The net benefit of the proposed strategy was superior to that of the select-all and select-none strategies according to decision curve analysis. Therefore, when chest CT scans were performed with a risk of a positive result ≥10%, 532 (59.8%) negative chest CT scans could be prevented. Only 24 (2.7%) potentially positive chest CT scans were misdiagnosed. Therefore, we recommend chest CT in patients with ≥cT1b disease, CCI ≥4, and low AGR.

Predicting the aggravation of coronavirus disease-19 pneumonia using chest computed tomography scans.

Presently, coronavirus disease-19 (COVID-19) is spreading worldwide without an effective treatment method. For COVID-19, which is often asymptomatic, it is essential to adopt a method that does not cause aggravation, as well as a method to prevent infection. Whether aggravation can be predicted by analyzing the extent of lung damage on chest computed tomography (CT) scans was examined. The extent of lung damage on pre-intubation chest CT scans of 277 patients with COVID-19 was assessed. It was observed that aggravation occurred when the CT scan showed extensive damage associated with ground-glass opacification and/or consolidation (p < 0.0001). The extent of lung damage was similar across the upper, middle, and lower fields. Furthermore, upon comparing the extent of lung damage based on the number of days after onset, a significant difference was found between the severe pneumonia group (SPG) with intubation or those who died and non-severe pneumonia group (NSPG) ≥3 days after onset, with aggravation observed when ≥14.5% of the lungs exhibited damage at 3-5 days (sensitivity: 88.2%, specificity: 72.4%) and when ≥20.1% of the lungs exhibited damage at 6-8 days (sensitivity: 88.2%, specificity: 69.4%). Patients with aggravation suddenly developed hypoxemia after 7 days from the onset; however, chest CT scans obtained in the paucisymptomatic phase without hypoxemia indicated that subsequent aggravation could be predicted based on the degree of lung damage. Furthermore, in subjects aged ≥65 years, a significant difference between the SPG and NSPG was observed in the extent of lung damage early beginning from 3 days after onset, and it was found that the degree of lung damage could serve as a predictor of aggravation. Therefore, to predict and improve prognosis through rapid and appropriate management, evaluating patients with factors indicating poor prognosis using chest CT is essential.

MFL-Net: An Efficient Lightweight Multi-Scale Feature Learning CNN for COVID-19 Diagnosis From CT Images.

Timely and accurate diagnosis of coronavirus disease 2019 (COVID-19) is crucial in curbing its spread. Slow testing results of reverse transcription-polymerase chain reaction (RT-PCR) and a shortage of test kits have led to consider chest computed tomography (CT) as an alternative screening and diagnostic tool. Many deep learning methods, especially convolutional neural networks (CNNs), have been developed to detect COVID-19 cases from chest CT scans. Most of these models demand a vast number of parameters which often suffer from overfitting in the presence of limited training data. Moreover, the linearly stacked single-branched architecture based models hamper the extraction of multi-scale features, reducing the detection performance. In this paper, to handle these issues, we propose an extremely lightweight CNN with multi-scale feature learning blocks called as MFL-Net. The MFL-Net comprises a sequence of MFL blocks that combines multiple convolutional layers with 3 ×3 filters and residual connections effectively, thereby extracting multi-scale features at different levels and preserving them throughout the block. The model has only 0.78M parameters and requires low computational cost and memory space compared to many ImageNet pretrained CNN architectures. Comprehensive experiments are carried out using two publicly available COVID-19 CT imaging datasets. The results demonstrate that the proposed model achieves higher performance than pretrained CNN models and state-of-the-art methods on both datasets with limited training data despite having an extremely lightweight architecture. The proposed method proves to be an effective aid for the healthcare system in the accurate and timely diagnosis of COVID-19.

A study on changes in lung function, neutralizing antibodies, and symptoms of adult patients hospitalized with COVID-19.

To identify changes in symptoms and pulmonary sequelae in patients with coronavirus disease 2019 (COVID-19). Patients with COVID-19 hospitalized at seven university hospitals in Korea between February 2020 and February 2021 were enrolled, provided they had ≥ 1 outpatient follow-up visit. Between January 11 and March 9, 2021 (study period), residual symptom investigations, chest computed tomography (CT) scans, pulmonary function tests (PFT), and neutralizing antibody tests (NAb) were performed at the outpatient visit (cross-sectional design). Additionally, data from patients who already had follow-up outpatient visits before the study period were collected retrospectively. Investigation of residual symptoms, chest CT scans, PFT, and NAb were performed in 84, 35, 31, and 27 patients, respectively. After 6 months, chest discomfort and dyspnea persisted in 26.7% (4/15) and 33.3% (5/15) patients, respectively, and 40.0% (6/15) and 26.7% (4/15) patients experienced financial loss and emotional distress, respectively. When the ratio of later CT score to previous ones was calculated for each patient between three different time intervals (1-14, 15-60, and 61-365 days), the median values were 0.65 (the second interval to the first), 0.39 (the third to the second), and 0.20 (the third to the first), indicating that CT score decreases with time. In the high-severity group, the ratio was lower than in the low-severity group. In COVID-19 survivors, chest CT score recovers over time, but recovery is slower in severely ill patients. Subjects complained of various ongoing symptoms and socioeconomic problems for several months after recovery.

Using Routine Chest Computed Tomography to Diagnose Pulmonary Embolism.

This study investigated the use of routine contrast-enhanced chest computed tomography (CT) to diagnose unsuspected pulmonary embolism (PE). All adult routine contrast-enhanced chest CTs performed at Montefiore in 2018 were included. Pulmonary artery enhancement was measured by placing regions of interest in the pulmonary vasculature. Adequate enhancement was defined as 200 Hounsfield units (HU) or greater. Presence or absence of PE was noted. Descriptive statistics and logistic regression analysis were performed. A total of 3164 CTs were evaluated (55.8% women; mean age, 63.2 years). Main pulmonary enhancement was highly correlated with peripheral enhancement. Of all cases, 28.7% (907 of 3164) reached the 200 HU threshold. Greater enhancement was associated with female sex, older age, outpatients, and contrast amount administered. Pulmonary embolism-positive cases comprised 1.8% (58 of 3164) of total cases. Furthermore, 39.7% (23 of 58) of PE-positive cases reached the 200 HU threshold. Over one quarter of routine contrast-enhanced chest CT scans met the 200 HU threshold indicative of adequate pulmonary artery enhancement, including nearly half of the 2% of examinations positive for PE.

Mid-term subclinical myocardial injury detection in patients who recovered from COVID-19 according to pulmonary lesion severity

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV 2) may cause damage to the cardiovascular system during the acute phase of the infection. However, recent studies reported mid- to long-term subtle cardiac injuries after recovering from acute coronavirus disease 2019 (COVID-19). This study aimed to determine the relationship between the severity of chest computed tomography (CT) lesions and the persistence of subtle myocardial injuries at mid-term follow-up of patients who recovered from COVID-19 infection. All patients with COVID-19 were enrolled prospectively in this study. Sensitive troponin T (hsTnT) and chest CT scans were performed on all patients during the acute phase of COVID-19 infection. At the mid-term follow-up, conventional transthoracic echocardiograph and global longitudinal strain (GLS) of the left and right ventricles (LV and RV) were determined and compared between patients with chest CT scan lesions of < 50% (Group 1) and those with severe chest CT scan lesions of greater or equal to 50% (Group 2). The mean age was 55 ± 14 years. Both LV GLS and RV GLS values were significantly decreased in group 2 (p = 0.013 and p = 0.011, respectively). LV GLS value of more than -18 was noted in 43% of all the patients, and an RV GLS value of more than -20 was observed in 48% of them. The group with severe chest CT scan lesions included more patients with reduced LV GLS and reduced RV GLS than the group with mild chest CT scan lesions [(G1:29 vs. G2:57%, p = 0.002) and (G1:36 vs. G2:60 %, p = 0.009), respectively]. Patients with severe chest CT scan lesions are more likely to develop subclinical myocardial damage. Transthoracic echocardiography (TTE) could be recommended in patients recovering from COVID-19 to detect subtle LV and RV lesions.

Deep Transfer Learning for COVID-19 Detection and Lesion Recognition Using Chest CT Images.

Starting from December 2019, the global pandemic of coronavirus disease 2019 (COVID-19) is continuously expanding and has caused several millions of deaths worldwide. Fast and accurate diagnostic methods for COVID-19 detection play a vital role in containing the plague. Chest computed tomography (CT) is one of the most commonly used diagnosis methods. However, a complete CT-scan has hundreds of slices, and it is time-consuming for radiologists to check each slice to diagnose COVID-19. This study introduces a novel method for fast and automated COVID-19 diagnosis using the chest CT scans. The proposed models are based on the state-of-the-art deep convolutional neural network (CNN) architecture, and a 2D global max pooling (globalMaxPool2D) layer is used to improve the performance. We compare the proposed models to the existing state-of-the-art deep learning models such as CNN based models and vision transformer (ViT) models. Based off of metric such as area under curve (AUC), sensitivity, specificity, accuracy, and false discovery rate (FDR), experimental results show that the proposed models outperform the previous methods, and the best model achieves an area under curve of 0.9744 and accuracy 94.12% on our test datasets. It is also shown that the accuracy is improved by around 1% by using the 2D global max pooling layer. Moreover, a heatmap method to highlight the lesion area on COVID-19 chest CT images is introduced in the paper. This heatmap method is helpful for a radiologist to identify the abnormal pattern of COVID-19 on chest CT images. In addition, we also developed a freely accessible online simulation software for automated COVID-19 detection using CT images. The proposed deep learning models and software tool can be used by radiologist to diagnose COVID-19 more accurately and efficiently.

Estimation of biochemical factors affecting survival in intensive care COVID-19 patients undergoing chest CT scoring: A retrospective cross-sectional study.

Coronavirus disease 2019 (COVID-19) is a rapidly spreading deadly respiratory disease that emerged in the city of Wuhan in December 2019. As a result of its rapid and widespread transmission, the WHO declared a pandemic on March 11, 2020 and studies evaluating mortality and prognosis in COVID-19 gained importance. The aim of this study was to determine the factors affecting the survival of COVID-19 patients followed up in a tertiary intensive care unit (ICU) and undergoing chest computed tomography (CT) scoring. This retrospective cross-sectional study was conducted with the approval of Uşak University Medical Faculty Ethics Committee between July and September 2020. It included 187 symptomatic patients (67 females, 120 males) with suspected COVID-19 who underwent chest CT scans in the ICU. Demographics, acute physiology and chronic health evaluation (APACHE II), chest CT scores, COVID-19 real-time polymerase chain reaction (RT PCR) results, and laboratory parameters were recorded. SPSS 15.0 for Windows was used for the data analysis. The ages of the patients ranged from 18 to 94 and the mean age was 68.0 ± 13.9 years. The COVID-19 RT PCR test was positive in 86 (46.0%) patients and 110 patients (58.8%) died during the follow-up. ICU stay (P = .024) and total invasive mechanical ventilation time (P < .001) were longer and blood urea nitrogen (BUN) was higher (P < .001) in the nonsurvivors. Patients with an APACHE II score of 23 and above had a 1.12-fold higher mortality rate (95% CI 0.061–0.263). There was no significant difference in total chest CT score between the survivors and nonsurvivors (P = .210). Chest CT score was not significantly associated with mortality in COVID-19 patients. Our idea that COVID-19 will cause greater mortality in patients with severe chest CT findings has changed. More studies on COVID-19 are needed to reveal the markers that affect prognosis and mortality in this period when new variants are affecting the world.

A deep learning-based post-processing method for automated pulmonary lobe and airway trees segmentation using chest CT images in PET/CT.

BackgroundThe proposed algorithm could support accurate localization of lung disease. To develop and validate an automated deep learning model combined with a post-processing algorithm to segment six pulmonary anatomical regions in chest computed tomography (CT) images acquired during positron emission tomography/computed tomography (PET/CT) scans. The pulmonary regions have five pulmonary lobes and airway trees.MethodsPatients who underwent both PET/CT imaging with an extra chest CT scan were retrospectively enrolled. The pulmonary segmentation of six regions in CT was performed via a convolutional neural network (CNN) of DenseVNet architecture with some post-processing algorithms. Three evaluation metrics were used to assess the performance of this method, which combined deep learning and the post-processing method. The agreement between the combined model and ground truth segmentations in the test set was analyzed.ResultsA total of 640 cases were enrolled. The combined model, which involved deep learning and post-processing methods, had a higher performance than the single deep learning model. In the test set, the all-lobes overall Dice coefficient, Hausdorff distance, and Jaccard coefficient were 0.972, 12.025 mm, and 0.948, respectively. The airway-tree Dice coefficient, Hausdorff distance, and Jaccard coefficient were 0.849, 32.076 mm, and 0.815, respectively. A good agreement was observed between our segmentation in every plot.ConclusionsThe proposed model combining two methods can automatically segment five pulmonary lobes and airway trees on chest CT imaging in PET/CT. The performance of the combined model was higher than the single deep learning model in each region in the test set.

Postoperative nosocomial COVID-19 infection in surgical patients during pandemic: A prospective observational cohort study.

Background: increased pressure on healthcare systems and possible risk of nosocomial COVID-19 infection during pandemic urged many guidelines to severely restrict the number of operations. The aim of this study was to investigate the risk of COVID-19 infection and its complications in patients undergoing urgent or elective operations.Methods: a prospective observational cohort study was conducted in a tertiary surgical center and all patients with no preoperative history of COVID-19 undergoing elective or emergent surgeries were included in this investigation. chest computed tomography (CT) scan or polymerase chain reaction (PCR) test were performed on patients before and after surgery.Results183 patients who underwent an operation were enrolled in this study. In postoperative follow-up, 12 patients were positive for COVID-19 infection as identified by RT-PCR and non-contrasted chest CT scans. Regrettably, 2 individuals passed with one of these individuals dying as a direct result of COVID-19 infection. All the 12 cases of post-operative COVID-19 patients underwent elective surgeries.Conclusionthe gathered results indicate a need for the re-evaluation of the risks of operation during the COVID-19 pandemic. If operations are performed while observing protective and preventative protocols, the risk of post-operative nosocomial COVID-19 is significantly reduced. Hence, the consequences imposed on patients by the delay or cancellation of operations (most notably in cancer cases) may outweigh the risk of post-operative COVID-19 infections.

Examining the Validity of Input Lung CT Images Submitted to the AI-Based Computerized Diagnosis

A well-designed CAD tool should respond to input requests, user actions, and perform input checks. Thus, an important element of such a tool is the pre-processing of incoming data and screening out those data that cannot be processed by the application. In this paper, we consider non-trivial methods of chest computed tomography (CT) images verifications: modality and human chest checks. We review sources to develop training datasets, describe architectures of convolution neural networks (CNN), clarify pre-processing and augmentation processes of chest CT scans and show results of training. The developed application showed good results: 100% classification accuracy on the test dataset for modality check and 89% classification accuracy on the test dataset for checking of lungs presence. Analysis of wrong predictions showed that the model performs poorly on biopsy of lungs. In general, the developed input data validation model shows good results on the designed datasets for CT image modality check and for checking of lungs presence .

A direct comparison between five lung-US and chest-CT-scans in a patient infected by SARS-CoV-2

The gold standard for SARS-CoV-2 pneumonia diagnosis is chest Computed Tomography (CT), but Lung Ultrasound (LUS) is also useful in differential diagnosis and in-hospital monitoring of patients with infection by new Coronavirus 2019 disease (COVID- 19). We present a case of a young man who was infected with SARS-CoV-2 pneumoniae and underwent five steps of chest imaging, including LUS aeration scorings and chest CT scans. Each decrease or increase in LUS scoring could accurately predict CT scan changes.

Flexible bronchoscopy indications and outcomes between indigenous and non-indigenous patients in the Northern Territory of Australia.

There is sparse evidence in the literature in relation to indications and outcomes among adult indigenous patients requiring a flexible bronchoscopy (FB). In this study, FB indications and outcomes between indigenous and non-indigenous patients were assessed. To assess the similarities and differences of FB indications and outcomes between indigenous and non-indigenous patients. Self-reported indigenous status, resident locality and the primary indication for FB were assessed. The FB procedures details, results of microbiology, cytology and histopathology were compared between indigenous and non-indigenous patients. Chest computed tomography (CT) was also analysed for its relationship to FB outcomes. Of the 403 patients, 111 (28%) were indigenous, and indigenous patients were younger (mean difference 11 years) and had a higher proportion of remote residence (70% vs 13%). Malignancy (40%) and infection (31%) were the most common indications for FB, although indigenous patients reported significantly more haemoptysis (15% vs 9%). No differences were noted in findings of the preceding chest CT scans. For positive microbiology, indigenous patients had a higher presence of Streptococcus pneumoniae (30% vs 9%), while non-indigenous patients had a higher presence of Pseudomonas aeruginosa (43% vs 11%) and mycobacteria (15% vs 4%). There was no significant difference between indigenous and non-indigenous patients for a positive histopathology, particularly for a diagnosis of lung malignancy (58% vs 54%). This study has demonstrated that adult indigenous patients requiring a FB are significantly younger, tend to reside in remote communities and demonstrate differing microbiology with no significant difference in the diagnostic outcomes for lung malignancy. Ethnic status or remoteness should not preclude indigenous patients to undergo a FB if clinically indicated.

Follow-up the severity of abnormalities diagnosed in chest CT imaging of COVID-19 patients: A cross-sectional study.

Background and AimsThis study aimed to evaluate the severity of diagnosed lung abnormalities of coronavirus disease 2019 (COVID‐19) patients based on the pre‐and postrecovery follow‐up chest computed tomography (CT) scan findings done at regular intervals.MethodsThis cross‐sectional study was performed in three phases. The severity of lung abnormalities was recorded and compared based on the initial and follow‐up chest CT findings carried out pre‐and at regular intervals (3 and 6 months) of postrecovery of COVID‐19 patients. Statistical data analysis was conducted using SPSS‐Version 26. Pearson Chi‐square test was used to analyze the results. p‐value < 0.05 was considered statistically significant.ResultsRegarding the initial chest CT findings, although ground‐glass opacity (GGO) was observed as the most common lung lesion, almost all the evaluated COVID‐19 patients had multiple lung lesions and involvements, especially with more involvement of the lower lobes. concerning the frequency of lung lesions and involvements in all phases of the study, almost no statistically significant differences were observed between male and female COVID‐19 patients and different age groups. However, older age groups had relatively more lung abnormalities due to Covid‐19 based on initial CT images which take more time to be eliminated. Lung abnormalities of Covid‐19 patients decreased significantly during the follow ups based on chest CT findings at different study phases.ConclusionAccording to evaluated pre‐ and post‐recovery chest CT scans, the frequency of lung lesions and lung involvement distribution decreased significantly in COVID‐19 patients, 3 and 6 months after recovery, and most of the recovered patients had no lung lesions or involvement anymore.

Diagnostic Value of Chest CT scan for COVID-19 Suspected Cases and Its Compatibility with RT-PCR Method: A Survey from Jiroft, Iran

Introduction: Accurate diagnosis of the COVID-19 disease is important. Currently, chest computed tomography (CT) and reverse polymerase chain reaction (RT-PCR) are being used for the diagnosis of the COVID-19 disease. This study was performed to evaluate the Chest computed tomography (CT) diagnostic value in comparison with the RT - PCR method among COVID-19 patients. Methods: This cross-sectional study was performed on suspected cases of COVID-19 in Imam Khomeini Hospital, Jiroft, Iran. Studied patients were evaluated via both a chest CT scan and nasopharyngeal swab for SARS-CoV-2 detection. Data was collected using a self-administered checklist, including demographic information, medical history, and symptoms of COVID-19, chest CT scan, and RT-PCR findings. Data were analyzed using SPSS-V21. Results: One thousand and ninety (1090) cases participated in the study; the mean age of the cases of COVID-19 was 48.20± 7.31 years old. The results of the RT-PCR test were 410 (37.6%) positive and 680 (62.4%) negative cases. According to the results of RT-PCR, which is the gold standard method, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive values of chest CT were 98.5%. (99.4-96.8 CI: 95%), 55.7% (59.5 – 51.9 CI: 95%), 71.5% (74.4 -69.0 CI: 95%), 57.3% (60.9 – 53.5 CI: 95%), and 98.4% (99.4% - 99.6 CI: 95%), respectively. Discussion: The results of the present study showed that a chest CT scan is highly sensitive for the diagnosis of the COVID-19 disease. Therefore, it can be used as a suitable method for screening and early detection, which requires knowledge of its common radiologic patterns. However, the results showed that the use of this method has low specificity, so it cannot be used for definitive diagnosis and should be used as a complementary method concomitant to the RT - PCR test.

Evaluation of the appropriate use of chest CT-Scans in the diagnosis of hospitalized patients in shiraz teaching hospitals, Southern Iran

PurposeDuring recent years, overuse of medical imaging especially computed tomography has become a serious concern. We evaluated the suitable usage of chest computed tomography (CT)-scan, in patients hospitalized in emergency and medical wards of two teaching hospitals of Shiraz University of Medical Science.MethodsMedical records of 216 patients admitted in two major teaching hospitals (Namazi and Shahid Faghihi), who had undergone chest radiography and at least one type of chest CT were investigated. The clinical and paraclinical manifestations were independently presented to three pulmonologists and their opinion regarding the necessity and type of CT prescription were documented. Also, the patient’s history was presented to an expert chest radiologist and asked to rate the appropriateness of chest CT according to American colleague of radiologist (ACR) criteria.ResultsIn 127 cases (59%), at least 2 out of 3 pulmonologists had the same opinion on the necessity of performing CT scan regardless of CT scan type, in 89 cases (41%) the same CT type and in 38 (17.5%) cases other CT type was supposed. Based on ACR criteria, of total prescribed CTs, 49.5% were “usually not appropriate” and 31.5% of cases were “usually appropriate”. Among 109 pulmonary CT angiography, 54 (49.5%) was usually not appropriate base on ACR criteria, which was the most frequent inappropriate requested CT type.ConclusionConsidering the high rates of inappropriate utilization of chest CT scan in our teaching hospitals, implementation of the standard guideline at a different level and consulting with a pulmonologist, may prevent unnecessary chest CTs prescription and reduce harm to patients and the health system.

Imaging features of radiation-induced lung disease and its relationship with clinical and dosimetric factors in breast cancer patients.

The aim is to extensively evaluate imaging features of radiation induced lung disease in breast cancer patients and to determine the relationship of imaging alterations with dosimetric parameters and patient related characteristics. A total of 76 breast cancer patients undergoing radiotherapy (RT) were studied retrospectively by case notes, treatment plans, dosimetric parameters, and chest computed tomography (CT) scans. Time intervals, that chest CT scans were acquired, were grouped as 1-6 months, 7-12 months, 13-18 months and more than 18 months after RT. Chest CTs (one or more for each patient) were assessed for the presence of ground glass opacity, septal thickening, consolidation/patchy pulmonary opacity/alveolar infiltrates, subpleural air cyst, air bronchogram, parenchymal bands, traction bronchiectasis, pleural/subpleural thickening and pulmonary volume loss. These alterations were scored by applying a system devised by Nishioka et al. Nishioka scores were analyzed for the relationship with clinical and dosimetric factors. IBM SPSS Statistics for Windows, version 22.0 (IBM Corp., Armonk, N.Y., USA) was used to analyze data. Median follow-up time was 49 months. Advanced age and aromatase inhibitor intake were correlated with higher Nishioka scores for 1-6 months' period. However, both were found nonsignificant in multivariate analysis. Nishioka scores of CT scans acquired more than 12 months after RT were positively correlated with mean lung dose, V5, V20, V30, and V40. Receiver operating characteristic analysis revealed that V5 for ipsilateral lung was the most robust dosimetric parameter predicting chronic lung injury. V5 >41% indicates the development of radiological lung changes. Keeping V5 ≤41% for ipsilateral lung could provide avoiding chronic lung sequelae.

"Digital Tomosynthesis" As a Technique for the Evaluation of Endobronchial Stents in Lung Transplant Recipients.

Lung transplant patients often suffer from posttransplant airway pathologies that require placement of endobronchial stents. In addition to surveillance bronchoscopy, patients often undergo radiographic stent evaluations. Chest x-rays are extremely limited in their ability to diagnose stent complications, so many patients require chest computed tomography (CT) scans for stent evaluation. Chest CT scans are costly and expose patients to higher cumulative radiation doses. Digital tomosynthesis (DTS) is an imaging modality that provides high-resolution images using limited angle tomography. The costs and radiation doses are comparable to conventional x-ray. We present a series of 4 postlung transplant patients with bronchial stents in whom we performed DTS and chest x-ray simultaneously. The DTS images were far superior to chest x-ray and comparable with CT in evaluating the placement and patency of the stents, especially in the case of silicone stents. Furthermore, the improved resolution provided clinically relevant diagnostic information that resulted in therapeutic bronchoscopy for suctioning of mucus impaction in one of the patients.

Residual Lung Parenchymal CT Changes in COVID-19 Patients and its Association with Common Predictors

Introduction: Chest computed tomography (CT) is used for the screening and diagnosis of COVID-19 pneumonia. The aim of the study is to study the prevalence of residual lung parenchymal CT changes in COVID-19 patients and its association with common predictors. Methods: Longitudinal study conducted at Shree Birendra Hospital in which diagnosed COVID-19 patients with initial CT scan showing COVID-19 related changes were retrospectively enrolled after ethics approval. The study period was from 15 February 2020 to 15 November 2021. Chest CT changes were evaluated and CT scan severity score (CT SS) assigned. Data was entered in Microsoft Excel and analysed in SPSS (Statistical Package for Social Sciences) version 16. Results: Males were more common (72.6%) among the 62 enrolled participants with the mean age 53.63 ± 15.05 years. Median interval between two scans was 137 days. Residual lung parenchymal CT changes were seen in 43 (69.4%) participants. Hypertension (25, 40.3%) and GGO (55, 88.7%) were the most common comorbidity and CT features respectively. The mean initial and final CT SS was 10.84 ± 5.87 and 3.55 ± 4.58 respectively (Total score 25, p = 0.001). Age was significantly associated with severity of CT score (p = 0.04). Conclusions: A large percentage of COVID-19 patients have chest CT changes after three months of initial chest CT scan. CT SS reduced significantly in the second scan. Similar studies with long term serial CT follow up of patients is recommended to be carried out in future.