Accuracy of Ultralow-Dose Photon-counting CT in the Detection of Lung Changes after Lung Transplant.

Background Data on the diagnostic accuracy of ultralow-dose (ULD) CT protocols for periodic surveillance in recipients of lung transplant are lacking. Purpose To assess the potential for radiation dose reduction using ULD photon-counting CT (PCT) to detect lung abnormalities in recipients of lung transplant during repeat CT follow-up. Materials and Methods Consecutive adult recipients of lung transplant undergoing same-day standard-of-care low-dose (LD) and ULD PCT from March 2023 to May 2023 were prospectively included. The ULD protocols were performed with two target effective doses comprising 20% (hereafter, ULD1) and 10% (hereafter, ULD2) of the standard LD protocol. The 1-mm reconstructions were reviewed by three readers. Subjective image quality, the visibility of certain anatomic structures (using a five-point Likert scale), and the presence of lung abnormalities were independently assessed. The χ2 or t tests were used to evaluate differences between the ULD1 and ULD2 protocols. Results A total of 82 participants (median age, 64 years [IQR, 54-69 years]; 47 male) were included (41 participants for each ULD protocol). The mean effective doses per protocol were 1.41 mSv ± 0.44 (SD) for LD, 0.26 mSv ± 0.08 for ULD1, and 0.17 mSv ± 0.04 for ULD2. According to three readers, the subjective image quality of the ULD images was deemed diagnostic (Likert score ≥3) in 39-40 (ULD1) and 40-41 (ULD2) participants, and anatomic structures could be adequately visualized (Likert score ≥3) in 33-41 (ULD1) and 34-41 (ULD2) participants. The detection accuracy for individual lung anomalies exceeded 70% for both ULD protocols, except for readers 1 and 3 detecting proximal bronchiectasis and reader 3 detecting bronchial wall thickening and air trapping. No evidence of a statistically significant difference in noise (P = .96), signal-to-noise ratio (P = .77), or reader accuracy (all P ≥ .05) was noted between the ULD protocols. Conclusion ULD PCT was feasible for detecting lung abnormalities following lung transplant, with a tenfold radiation dose reduction. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ciet in this issue.

Ultra-low-dose vs. standard-of-care-dose CT of the chest in patients with post-COVID-19 conditions-a prospective intra-patient multi-reader study.

OBJECTIVES: To conduct an intrapatient comparison of ultra-low-dose computed tomography (ULDCT) and standard-of-care-dose CT (SDCT) of the chest in terms of the diagnostic accuracy of ULDCT and intrareader agreement in patients with post-COVID conditions. METHODS: We prospectively included 153 consecutive patients with post-COVID-19 conditions. All participants received an SDCT and an additional ULDCT scan of the chest. SDCTs were performed with standard imaging parameters and ULDCTs at a fixed tube voltage of 100 kVp (with tin filtration), 50 ref. mAs (dose modulation active), and iterative reconstruction algorithm level 5 of 5. All CT scans were separately evaluated by four radiologists for the presence of lung changes and their consistency with post-COVID lung abnormalities. Radiation dose parameters and the sensitivity, specificity, and accuracy of ULDCT were calculated. RESULTS: Of the 153 included patients (mean age 47.4 ± 15.3 years; 48.4% women), 45 (29.4%) showed post-COVID lung abnormalities. In those 45 patients, the most frequently detected CT patterns were ground-glass opacities (100.0%), reticulations (43.5%), and parenchymal bands (37.0%). The accuracy, sensitivity, and specificity of ULDCT compared to SDCT for the detection of post-COVID lung abnormalities were 92.6, 87.2, and 94.9%, respectively. The median total dose length product (DLP) of ULDCTs was less than one-tenth of the radiation dose of our SDCTs (12.6 mGy*cm [9.9; 15.5] vs. 132.1 mGy*cm [103.9; 160.2]; p < 0.001). CONCLUSION: ULDCT of the chest offers high accuracy in the detection of post-COVID lung abnormalities compared to an SDCT scan at less than one-tenth the radiation dose, corresponding to only twice the dose of a standard chest radiograph in two views. CLINICAL RELEVANCE STATEMENT: Ultra-low-dose CT of the chest may provide a favorable, radiation-saving alternative to standard-dose CT in the long-term follow-up of the large patient cohort of post-COVID-19 patients.

Contrast Agent Dynamics Determine Radiomics Profiles in Oncologic Imaging.

BACKGROUND: The reproducibility of radiomics features extracted from CT and MRI examinations depends on several physiological and technical factors. The aim was to evaluate the impact of contrast agent timing on the stability of radiomics features using dynamic contrast-enhanced perfusion CT (dceCT) or MRI (dceMRI) in prostate and lung cancers. METHODS: Radiomics features were extracted from dceCT or dceMRI images in patients with biopsy-proven peripheral prostate cancer (pzPC) or biopsy-proven non-small cell lung cancer (NSCLC), respectively. Features that showed significant differences between contrast phases were identified using linear mixed models. An L2-penalized logistic regression classifier was used to predict class labels for pzPC and unaffected prostate regions-of-interest (ROIs). RESULTS: Nine pzPC and 28 NSCLC patients, who were imaged with dceCT and/or dceMRI, were included in this study. After normalizing for individual enhancement patterns by defining seven individual phases based on a reference vessel, 19, 467 and 128 out of 1204 CT features showed significant temporal dynamics in healthy prostate parenchyma, prostate tumors and lung tumors, respectively. CT radiomics-based classification accuracy of healthy and tumor ROIs was highly dependent on contrast agent phase. For dceMRI, 899 and 1027 out of 1118 features were significantly dependent on time after contrast agent injection for prostate and lung tumors. CONCLUSIONS: CT and MRI radiomics features in both prostate and lung tumors are significantly affected by interindividual differences in contrast agent dynamics.

Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training.

Liver vessel segmentation in magnetic resonance imaging data is important for the computational analysis of vascular remodeling, associated with a wide spectrum of diffuse liver diseases. Existing approaches rely on contrast enhanced imaging data, but the necessary dedicated imaging sequences are not uniformly acquired. Images without contrast enhancement are acquired more frequently, but vessel segmentation is challenging, and requires large-scale annotated data. We propose a multi-task learning framework to segment vessels in liver MRI without contrast. It exploits auxiliary contrast enhanced MRI data available only during training to reduce the need for annotated training examples. Our approach draws on paired native and contrast enhanced data with and without vessel annotations for model training. Results show that auxiliary data improves the accuracy of vessel segmentation, even if they are not available during inference. The advantage is most pronounced if only few annotations are available for training, since the feature representation benefits from the shared task structure. A validation of this approach to augment a model for brain tumor segmentation confirms its benefits across different domains. An auxiliary informative imaging modality can augment expert annotations even if it is only available during training.

Induction of Arterial Inflammation by Immune Checkpoint Inhibitor Therapy in Lung Cancer Patients as Measured by 2-[18F]FDG Positron Emission Tomography/Computed Tomography Depends on Pre-Existing Vascular Inflammation.

BACKGROUND: Immune checkpoint inhibitors (ICI) are one of the most effective therapies in oncology, albeit associated with various immune-related adverse events also affecting the cardiovascular system. METHODS: We aimed to investigate the effect of ICI on arterial 2-[18F]FDG uptake by using 2-[18F]FDG PET/CT imaging pre/post treatment in 47 patients with lung cancer. Maximum 2-[18F]FDG standardized uptake values (SUVmax) and target-to-background ratios (TBRs) were calculated along six arterial segments. We classified the arterial PET lesions by pre-existing active inflammation (cut-off: TBRpre ≥ 1.6). 2-[18F]FDG metabolic activity pre/post treatment was also quantified in bone marrow, spleen, and liver. Circulating blood biomarkers were additionally collected at baseline and after immunotherapy. RESULTS: ICI treatment resulted in significantly increased arterial inflammatory activity, detected by increased TBRs, in all arterial PET lesions analyzed. In particular, a significant elevation of arterial 2-[18F]FDG uptake was only recorded in PET lesions without pre-existing inflammation, in calcified as well as in non-calcified lesions. Furthermore, a significant increase in arterial 2-[18F]FDG metabolic activity after immunotherapy was solely observed in patients not previously treated with chemotherapy or radiotherapy as well as in those without CV risk factors. No significant changes were recorded in either 2-[18F]FDG uptake of bone marrow, spleen and liver after treatment, or the blood biomarkers. CONCLUSIONS: ICI induces vascular inflammation in lung cancer patients lacking pre-existing arterial inflammation.

Metabolic tumor volume and sites of organ involvement predict outcome in NSCLC immune-checkpoint inhibitor therapy.

PURPOSE: The purpose of this study was to assess the ability of pretreatment PET parameters and peripheral blood biomarkers to predict progression-free survival (PFS) and overall survival (OS) in NSCLC patients treated with ICIT. METHODS: We prospectively included 87 patients in this study who underwent pre-treatment [18F]-FDG PET/CT. Organ-specific and total metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were measured using a semiautomatic software. Sites of organ involvement (SOI) were assessed by PET/CT. The log-rank test and Cox-regression analysis were used to assess associations between clinical, laboratory, and imaging parameters with PFS and OS. Time dependent ROC were calculated and model performance was evaluated in terms of its clinical utility. RESULTS: MTV increased with the number of SOI and was correlated with neutrophil and lymphocyte cell count (Spearman's rho = 0.27 or 0.32; p =.02 or 0.003; respectively). Even after adjustment for known risk factors, such as PD-1 expression and neutrophil cell count, the MTV and the number of SOI were independent risk factors for progression (per 100 cm3; adjusted hazard ratio [aHR]: 1.13; 95% confidence interval [95%CI]: 1.01-1.28; p =.04; single SOI vs. ≥ 4 SOI: aHR: 2.26, 95%CI: 1.04-4.94; p =.04). MTV and the number of SOI were independent risk factors for overall survival (per 100 cm3 aHR: 1.11, 95%CI: 1.01-1.23; p =.03; single SOI vs. ≥ 4 SOI: aHR: 4.54, 95%CI: 1.64-12.58; p =.04). The combination of MTV and the number of SOI improved the risk stratification for PFS and OS (log-rank test p <.001; C-index: 0.64 and 0.67). CONCLUSION: The MTV and the number of SOI are simple imaging markers that provide complementary information to facilitate risk stratification in NSCLC patients scheduled for ICIT.

Hughes-Stovin syndrome-An important differential diagnosis in patients with suspected chronic thromboembolic pulmonary hypertension : A case report.

Hughes-Stovin syndrome (HSS) is a rare vasculitis of unknown etiology. The disease is characterized by pronounced inflammation and damage to the vessel walls, with subsequent widespread vascular thrombosis and the formation of pulmonary artery aneurysms that can lead to fatal hemoptysis. This disorder can be mistaken for other conditions, such as chronic thromboembolic pulmonary disease (CTEPD) without or with pulmonary hypertension at rest (CTEPH).We report the case of a 20-year-old female with HSS, which was misdiagnosed as CTEPH and subsequently treated with anticoagulants, which led to severe hemoptysis and eventually death of the patient. This case highlights the challenges of diagnosing HSS at early stages of the disease.HSS should be considered in young patients with signs of large vessel vasculitis in combination with thrombotic occlusions of pulmonary arteries, with or without aneurysms of the pulmonary arteries, and particularly, if there are no risk factors for thromboembolic disease.

Deep learning for [18F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis.

BACKGROUND: The rising global cancer burden has led to an increasing demand for imaging tests such as [18F]fluorodeoxyglucose ([18F]FDG)-PET-CT. To aid imaging specialists in dealing with high scan volumes, we aimed to train a deep learning artificial intelligence algorithm to classify [18F]FDG-PET-CT scans of patients with lymphoma with or without hypermetabolic tumour sites. METHODS: In this retrospective analysis we collected 16 583 [18F]FDG-PET-CTs of 5072 patients with lymphoma who had undergone PET-CT before or after treatment at the Memorial Sloa Kettering Cancer Center, New York, NY, USA. Using maximum intensity projection (MIP), three dimensional (3D) PET, and 3D CT data, our ResNet34-based deep learning model (Lymphoma Artificial Reader System [LARS]) for [18F]FDG-PET-CT binary classification (Deauville 1-3 vs 4-5), was trained on 80% of the dataset, and tested on 20% of this dataset. For external testing, 1000 [18F]FDG-PET-CTs were obtained from a second centre (Medical University of Vienna, Vienna, Austria). Seven model variants were evaluated, including MIP-based LARS-avg (optimised for accuracy) and LARS-max (optimised for sensitivity), and 3D PET-CT-based LARS-ptct. Following expert curation, areas under the curve (AUCs), accuracies, sensitivities, and specificities were calculated. FINDINGS: In the internal test cohort (3325 PET-CTs, 1012 patients), LARS-avg achieved an AUC of 0·949 (95% CI 0·942-0·956), accuracy of 0·890 (0·879-0·901), sensitivity of 0·868 (0·851-0·885), and specificity of 0·913 (0·899-0·925); LARS-max achieved an AUC of 0·949 (0·942-0·956), accuracy of 0·868 (0·858-0·879), sensitivity of 0·909 (0·896-0·924), and specificity of 0·826 (0·808-0·843); and LARS-ptct achieved an AUC of 0·939 (0·930-0·948), accuracy of 0·875 (0·864-0·887), sensitivity of 0·836 (0·817-0·855), and specificity of 0·915 (0·901-0·927). In the external test cohort (1000 PET-CTs, 503 patients), LARS-avg achieved an AUC of 0·953 (0·938-0·966), accuracy of 0·907 (0·888-0·925), sensitivity of 0·874 (0·843-0·904), and specificity of 0·949 (0·921-0·960); LARS-max achieved an AUC of 0·952 (0·937-0·965), accuracy of 0·898 (0·878-0·916), sensitivity of 0·899 (0·871-0·926), and specificity of 0·897 (0·871-0·922); and LARS-ptct achieved an AUC of 0·932 (0·915-0·948), accuracy of 0·870 (0·850-0·891), sensitivity of 0·827 (0·793-0·863), and specificity of 0·913 (0·889-0·937). INTERPRETATION: Deep learning accurately distinguishes between [18F]FDG-PET-CT scans of lymphoma patients with and without hypermetabolic tumour sites. Deep learning might therefore be potentially useful to rule out the presence of metabolically active disease in such patients, or serve as a second reader or decision support tool. FUNDING: National Institutes of Health-National Cancer Institute Cancer Center Support Grant.

Deep learning-based segmentation of multisite disease in ovarian cancer.

PURPOSE: To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. METHODS: A deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established "no-new-Net" framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test. RESULTS: Our model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (p values being 4 × 10-7, 3 × 10-4, 4 × 10-2, respectively), and for the omental lesions on the evaluation set (p = 1 × 10-3). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (p = 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions. CONCLUSION: Automated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions. RELEVANCE STATEMENT: Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines. KEY POINTS: • The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented. • Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists. • Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines.

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer.

High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.

Transversal psoas muscle thickness measurement is associated with response and survival in patients with HCC undergoing immunotherapy.

BACKGROUND: Sarcopenia is a common problem in patients with HCC. We aimed to evaluate the prognostic and predictive value of baseline transversal psoas muscle thickness (TPMT) measurement in patients with HCC undergoing immunotherapy. METHODS: HCC patients treated with programmed death ligand 1-based therapies between June 2016 and October 2022 at the Vienna General Hospital (n = 80) and the Hôpital Beaujon Clichy (n = 96) were included and followed until April 2023. TPMT at the level of the third lumbar vertebra was measured independently by 2 radiologists to evaluate interreader reliability. TPMT <12 mm/m in men and <8 mm/m in women indicated sarcopenia. RESULTS: Overall, 176 patients (age: 66.3±11.7 y; male: n=143, 81%, Barcelona-Clinic Liver Cancer C: n=121, 69%) were included, of which 131 (74%) exhibited cirrhosis. Interreader agreement for the diagnosis of sarcopenia based on TPMT was 92.6%, and Cohen κ showed a "strong agreement" [κ = 0.84 (95% CI: 0.75-0.92)]. Sarcopenia, present in 58 patients (33%), was associated with shorter median overall survival [7.2 (95% CI: 5.0-9.5) vs. 22.6 (95% CI: 16.4-28.8 months); p < 0.001] and median progression-free survival [3.4 (95% CI: 0.2-6.8) vs. 7.9 (95% CI: 5.8-9.9 months), p = 0.001], and an independent predictor of overall [adjusted HR: 1.63 (95% CI: 1.07-2.48)] and progression-free mortality [adjusted HR: 1.54 (95% CI: 1.06-2.23)] in multivariable analyses. The objective response rate [evaluable in 162 subjects (92.0%)] per modified Response Evaluation Criteria In Solid Tumors (mRECIST) in patients with and without sarcopenia was 22% and 39%, respectively (p = 0.029). Survival and radiological responses were worse in patients with sarcopenia and systemic inflammation [median overall survival: 6.1 (95% CI: 3.6-8.6) mo; median progression-free survival: 2.8 (95% CI: 2.1-3.4) mo; objective response rate=16%; disease control rate=39%]. CONCLUSIONS: Evaluation of sarcopenia using TPMT measurement is reliable and identifies HCC patients with a dismal prognosis and response to immunotherapy.

Imaging in patients with acute dyspnea when cardiac or pulmonary origin is suspected.

A wide spectrum of conditions, from life-threatening to non-urgent, can manifest with acute dyspnea, thus presenting major challenges for the treating physician when establishing the diagnosis and severity of the underlying disease. Imaging plays a decisive role in the assessment of acute dyspnea of cardiac and/or pulmonary origin. This article presents an overview of the current imaging modalities used to narrow the differential diagnosis in the assessment of acute dyspnea of cardiac or pulmonary origin. The current indications, findings, accuracy, and limits of each imaging modality are reported. Chest radiography is usually the primary imaging modality applied. There is a low radiation dose associated with this method, and it can assess the presence of fluid in the lung or pleura, consolidations, hyperinflation, pneumothorax, as well as heart enlargement. However, its low sensitivity limits the ability of the chest radiograph to accurately identify the causes of acute dyspnea. CT provides more detailed imaging of the cardiorespiratory system, and therefore, better sensitivity and specificity results, but it is accompanied by higher radiation exposure. Ultrasonography has the advantage of using no radiation, and is fast and feasible as a bedside test and appropriate for the assessment of unstable patients. However, patient-specific factors, such as body habitus, may limit its image quality and interpretability. Advances in knowledge This review provides guidance to the appropriate choice of imaging modalities in the diagnosis of patients with dyspnea of cardiac or pulmonary origin.

Neoadjuvant immune-checkpoint inhibitors in lung cancer - a primer for radiologists.

The introduction of neoadjuvant immune checkpoint inhibitors plus platinum-based chemotherapy has changed treatment regimens of patient's early-stage lung cancer. This treatment combination induces high rates of complete pathologic response and improves clinical endpoints. Imaging plays a fundamental role in assessment of treatment response, monitoring of (immune-related) adverse events and enables both the surgeon and pathologist optimal treatment and diagnostic workup of the resected tumor samples. Knowledge of the strengths and weaknesses of diagnostic imaging in this setting are essential for radiologists to provide valuable input in multidisciplinary team decisions.

[Mediastinum-new compartment classification]. / Mediastinum ­ neue Kompartimenteinteilung.

BACKGROUND: Mediastinal masses are common and comprise a heterogeneous spectrum of disorders. Correct diagnosis has prognostic and therapeutic consequences, which is why precise localization of lesions and interdisciplinary management are essential in clinical practice. This article describes traditional divisions of mediastinum lesions and presents the new classification based on cross-sectional imaging, which was developed by the International Thymic Malignancy Interest Group (ITMIG). OBJECTIVES: Which divisions of the mediastinum have been used so far and how does the division developed by the ITMIG differ? What are the advantages of the new mediastinal classification? MATERIALS AND METHODS: Comparison of the previously used mediastinal classification with the new mediastinal classification developed by ITMIG and visualization of the respective methods. In addition, pathologies typical for the respective compartments are explained. RESULTS AND CONCLUSION: The traditional compartmentalization of the mediastinum into an anterior, middle, and posterior mediastinum is not clearly defined and may lead to confusing interdisciplinary communication. Since these classifications are mostly based on projection radiographs, the proposed three-dimensional classification of the ITMIG is a development that suits the modern clinical workflow and promotes standardization. The three mediastinal compartments should thus be termed prevascular, visceral, and paravertebral.

Detection of Post-COVID-19 Lung Abnormalities: Photon-counting CT versus Same-Day Energy-integrating Detector CT.

Background Photon-counting detector (PCD) CT enables ultra-high-resolution lung imaging and may shed light on morphologic correlates of persistent symptoms after COVID-19. Purpose To compare PCD CT with energy-integrating detector (EID) CT for noninvasive assessment of post-COVID-19 lung abnormalities. Materials and Methods For this prospective study, adult participants with one or more COVID-19-related persisting symptoms (resting or exertional dyspnea, cough, fatigue) underwent same-day EID and PCD CT between April 2022 and June 2022. The 1.0-mm EID CT images and, subsequently, 1.0-, 0.4-, and 0.2-mm PCD CT images were reviewed for the presence of lung abnormalities. Subjective and objective EID and PCD CT image quality were evaluated using a five-point Likert scale (-2 to 2) and lung signal-to-noise ratios (SNRs). Results Twenty participants (mean age, 54 years ± 16 [SD]; 10 men) were included. EID CT showed post-COVID-19 lung abnormalities in 15 of 20 (75%) participants, with a median involvement of 10% of lung volume [IQR, 0%-45%] and 3.5 lobes [IQR, 0-5]. Ground-glass opacities and linear bands (10 of 20 participants [50%] for both) were the most frequent findings at EID CT. PCD CT revealed additional lung abnormalities in 10 of 20 (50%) participants, with the most common being bronchiectasis (10 of 20 [50%]). Subjective image quality was improved for 1.0-mm PCD versus 1.0-mm EID CT images (median, 1; IQR, 1-2; P < .001) and 0.4-mm versus 1.0-mm PCD CT images (median, 1; IQR, 1-1; P < .001) but not for 0.4-mm versus 0.2-mm PCD CT images (median, 0; IQR, 0-0.5; P = .26). PCD CT delivered higher lung SNR versus EID CT for 1.0-mm images (mean difference, 0.53 ± 0.96; P = .03) but lower SNR for 0.4-mm versus 1.0-mm images and 0.2-mm vs 0.4-mm images (-1.52 ± 0.68 [P < .001] and -1.15 ± 0.43 [P < .001], respectively). Conclusion Photon-counting detector CT outperformed energy-integrating detector CT in the visualization of subtle post-COVID-19 lung abnormalities and image quality. © RSNA, 2023 Supplemental material is available for this article.

Ultrahigh-Resolution Photon-Counting Detector CT of the Lungs: Association of Reconstruction Kernel and Slice Thickness With Image Quality.

BACKGROUND. Prior work has shown improved image quality for photon-counting detector (PCD) CT of the lungs compared with energy-integrating detector CT. A paucity of the literature has compared PCD CT of the lungs using different reconstruction parameters. OBJECTIVE. The purpose of this study is to the compare the image quality of ultra-high-resolution (UHR) PCD CT image sets of the lungs that were reconstructed using different kernels and slice thicknesses. METHODS. This retrospective study included 29 patients (17 women and 12 men; median age, 56 years) who underwent noncontrast chest CT from February 15, 2022, to March 15, 2022, by use of a commercially available PCD CT scanner. All acquisitions used UHR mode (1024 × 1024 matrix). Nine image sets were reconstructed for all combinations of three sharp kernels (BI56, BI60, and BI64) and three slice thicknesses (0.2, 0.4, and 1.0 mm). Three radiologists independently reviewed reconstructions for measures of visualization of pulmonary anatomic structures and pathologies; reader assessments were pooled. Reconstructions were compared with the clinical reference reconstruction (obtained using the BI64 kernel and a 1.0-mm slice thickness [BI641.0-mm]). RESULTS. The median difference in the number of bronchial divisions identified versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.5), BI600.4-mm (0.3), BI640.2-mm (0.5), and BI600.2-mm (0.2) (all p < .05). The median bronchial wall sharpness versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.3) and BI640.2-mm (0.3) and was lower for BI561.0-mm (-0.7) and BI560.4-mm (-0.3) (all p < .05). Median pulmonary fissure sharpness versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.3), BI600.4-mm (0.3), BI560.4-mm (0.5), BI640.2-mm (0.5), BI600.2-mm (0.5), and BI560.2-mm (0.3) (all p < .05). Median pulmonary vessel sharpness versus the clinical reference reconstruction was lower for reconstructions with BI561.0-mm (-0.3), BI600.4-mm (-0.3), BI560.4-mm (-0.7), BI640.2-mm (-0.7), BI600.2-mm (-0.7), and BI560.2-mm (-0.7). Median lung nodule conspicuity versus the clinical reference reconstruction was lower for reconstructions with BI561.0-mm (-0.3) and BI560.4-mm (-0.3) (both p < .05). Median conspicuity of all other pathologies versus the clinical reference reconstruction was lower for reconstructions with BI561.0 mm (-0.3), BI560.4-mm (-0.3), BI640.2-mm (-0.3), BI600.2-mm (-0.3), and BI560.2-mm (-0.3). Other comparisons among reconstructions were not significant (all p > .05). CONCLUSION. Only the reconstruction using BI640.4-mm yielded improved bronchial division identification and bronchial wall and pulmonary fissure sharpness without a loss in pulmonary vessel sharpness or conspicuity of nodules or other pathologies. CLINICAL IMPACT. The findings of this study may guide protocol optimization for UHR PCD CT of the lungs.

Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma.

Background: Pathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy response score (CRS) for omental tumor deposits. The main limitation of CRS is that it requires surgical sampling after initial neoadjuvant chemotherapy (NACT) treatment. Earlier and non-invasive response predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures to predict neoadjuvant response before NACT using CRS as a gold standard. Methods: Omental CT-based radiomics models, yielding a simplified fully interpretable radiomic signature, were developed using Elastic Net logistic regression and compared to predictions based on omental tumor volume alone. Models were developed on a single institution cohort of neoadjuvant-treated HGSOC (n = 61; 41% complete response to NCT) and tested on an external test cohort (n = 48; 21% complete response). Results: The performance of the comprehensive radiomics models and the fully interpretable radiomics model was significantly higher than volume-based predictions of response in both the discovery and external test sets when assessed using G-mean (geometric mean of sensitivity and specificity) and NPV, indicating high generalizability and reliability in identifying non-responders when using radiomics. The performance of a fully interpretable model was similar to that of comprehensive radiomics models. Conclusions: CT-based radiomics allows for predicting response to NACT in a timely manner and without the need for abdominal surgery. Adding pre-NACT radiomics to volumetry improved model performance for predictions of response to NACT in HGSOC and was robust to external testing. A radiomic signature based on five robust predictive features provides improved clinical interpretability and may thus facilitate clinical acceptance and application.

[Smoking-related interstitial lung diseases]. / Raucherassoziierte interstitielle Lungenerkrankungen.

CLINICAL ISSUE: Smoking-related interstitial lung diseases are a heterogeneous group of pulmonary abnormalities. The correct diagnosis has prognostic and therapeutic implications. This article introduces the most common smoking-related interstitial lung diseases and describes a structured approach to support the diagnostic workflow. PRACTICAL RECOMMENDATIONS: Computed tomography is pivotal in the diagnostic workflow of smoking-related interstitial lung diseases and may reduce the number of unnecessary lung biopsies. To achieve high diagnostic accuracy, a standardized scanning protocol, and a structured assessment approach should be utilized. During inflammatory stages of respiratory bronchiolitis (RB), respiratory bronchiolitis interstitial lung diseases (RB-ILD), and desquamative interstitial pneumonia (DIP), cessation of smoking as well as the use of steroids are the treatment of choice. In case of fibrotic changes (e.g., in idiopathic pulmonary fibrosis [IPF]), antifibrotic therapy with nintedanib and pirfenidone may be used. Patients with suspected smoking-related interstitial lung disease should be discussed in interdisciplinary board meetings.