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Background: The purpose of this article is to describe the various imaging techniques involved in detection, staging, and preoperative planning in malignant pleural mesothelioma (MPM) focusing on new imaging modalities., Methods: For this purpose, first a brief summary of the etiology of MPM is given. Second, not only the commonly known, but also novel imaging modalities used in MPM will be discussed., Results: A wide range of imaging methods, from conventional chest radiography, through computed tomography and hybrid imaging to radiomics and artificial intelligence, can be used to evaluate MPM., Conclusion: Nowadays multimodality imaging is considered the cornerstone in MPM diagnosis and staging., Competing Interests: None., (Thieme. All rights reserved.)

Background: Tobacco use is the leading preventable cause of cancer and cancer deaths. Tobacco use is not only related to lung cancer, but has an impact on a wide range of different cancer entities in almost every organ system., Objectives: The aim of this review article is to shed light on the different organ systems involved in tobacco-associated carcinogenesis., Materials and Methods: For this purpose, first a brief introduction into the topic is given, followed by a detailed description of the different tumor entities associated with tobacco use., Results: Tobacco consumption has been clearly implicated in the causation of many types of cancer, affecting multiple organ systems. Based on current evidence, tobacco use can cause cancer of the mouth and throat, larynx, esophagus, stomach, kidney, pancreas, liver, bladder, cervix, colon and rectum, and acute myeloid leukemia., Conclusion: Tobacco use is not only main cause in the development of lung cancer, but has a major impact in the development of cancer in other organ systems. Therefore, special attention must be given to possible concomitant malignancies when evaluating images of smokers., (© 2022. The Author(s).)

The aim of our study was to assess the influence of overlapping image reconstruction on thin-section chest computed tomography (CT) in patients with small lung nodules. In all, 40 patients with 128 pulmonary nodules underwent chest CT on a third-generation dual-source CT. All images were reconstructed with a section thickness of 1 mm and an increment of 0.5 to 1 mm. Two readers performed volume measurements with a commercially available software package. Our data indicate no relevant differences (P=0.44) for volume measurements of solid lung nodules comparing overlapping (50% overlap) and nonoverlapping image reconstructions with a mean difference of 0.5±2.3 mm3 and a mean absolute difference of nodule volume of 2±2% (range, 0% to 11%). Interobserver differences were 1.0±3.7 mm3 (reconstruction with 50 overlap) and 1.1±4.1 mm3 (nonoverlapping reconstructions). Differences in nodule volume measurements between overlapping and nonoverlapping image reconstructions were similar compared with interobserver differences. Obviating the reconstruction of overlapping sections for lung nodule volume measurements would reduce the duration of image reconstruction and amount of data storage., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: The purpose of this study was to assess whether the performances of an automated software for lung nodule detection with computed tomography (CT) are affected by radiation dose and the use of iterative reconstruction algorithm., Materials and Methods: A chest phantom (Multipurpose Chest Phantom N1; Kyoto Kagaku Co. Ltd, Kyoto, Japan) with 15 pulmonary nodules was scanned with a total of five CT protocol settings with up to 20-fold dose reduction. All CT examinations were reconstructed with iterative reconstruction algorithms ADMIRE 3 and ADMIRE 5 and were then analyzed for the presence of pulmonary nodules with a fully automated computer aided detection software system (InferRead TM CT Lung, Infervision), which is based on deep neural networks., Results: The sensitivity of fully automated pulmonary nodule detection for ground-glass nodules at standard dose CT was greater (70.0%; 14/20; 95% CI: 51.6-88.4%) than at 10-fold and 20-fold dose reduction (30.0%; 6/20; 95% CI: 0.0%-62.5%). There were less false positive findings when ADMIRE 5 reconstruction was used (4.0 ± 2.8 [SD]; range: 2-6) instead of ADMIRE 3 reconstruction (25.0 ± 15.6 [SD]; range: 14-36). There was no difference in the sensitivity of detection of solid and subsolid nodules between standard dose (100%; 95% CI: 100-100%) and 10- and 20-fold reduced dose CT (92.5%; 95% CI: 83.8-100.0%). Image noise was significantly greater with ADMIRE 3 (81 ± 2 [SD] [range: 79-84]; 104 ± 3 [SD] [range: 101-107]; 114 ± 5 [SD] [range: 110-119]; 193 ± 10 [SD] [range: 183-203]; 220 ± 16 [SD] [range: 210-238]) compared to ADMIRE 5 (44 ± 2 [SD] [range: 42-46]; 60 ± 2 [SD] [range: 57-61]; 66 ± 1 [SD] [range: 65-67]; 103 ± 4 [SD] [range: 98-106]; 110 ± 1 [SD] [range: 109-111]), respectively in each of the five CT protocols., Conclusion: This phantom study suggests that dose reduction and iterative reconstruction settings have an impact on detectability of pulmonary nodules by artificial intelligence software and we therefore encourage adaption of dose levels and reconstruction methods prior to widespread implementation of fully automatic nodule detection software for lung cancer screening purposes., (Copyright © 2021 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)

Rationale and Objectives: To assess if vessel suppression (VS) improves nodule detection rate, interreader agreement, and reduces reading time in oncologic chest computed tomography (CT)., Material and Methods: One-hundred consecutive oncologic patients (65 male; median age 60y) who underwent contrast-enhanced chest CT were retrospectively included. For all exams, additional VS series (ClearRead CT, Riverrain Technologies, Miamisburg) were reconstructed. Two groups of three radiologists each with matched experience were defined. Each group evaluated the SD-CT as well as VS-CT. Each reader marked the presence, size, and position of pulmonary nodules and documented reading time. In addition, for the VS-CT the presence of false positive nodules had to be stated. Cohen's Kappa (k) was used to calculate the interreader-agreement between groups. Reading time was compared using paired t test., Results: Nodule detection rate was significantly higher in VS-CT compared to the SD-CT (+21%; p <0.001). Interreader-agreement was higher in the VS-CT (k = 0.431, moderate agreement) compared to SD-CT (k = 0.209, fair agreement). Almost all VS-CT series had false positive findings (97-99 out of 100). Average reading time was significantly shorter in the VS-CT compared to the SD-CT (154 ± 134vs. 194 ± 126; 21%, p<0.001)., Conclusions: Vessel suppression increases nodule detection rate, improves interreader agreement, and reduces reading time in chest CT of oncologic patients. Due to false positive results a consensus reading with the SD-CT is essential., (Copyright © 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Objectives: Sarcopenia is associated with poor outcome in cancer-patients. However, the methods to define sarcopenia are not entirely standardized. We compared several morphometric measurements of sarcopenia and their prognostic value in short-term-outcome prediction after pneumonectomy., Material and Methods: Consecutive lung-cancer patients undergoing pneumonectomy from January 2007 to December 2015 and having a pre-operative computed tomography (CT) scan were retrospectively included. Sarcopenia was assessed by the following CT-based parameters measured at the level of the third lumbar vertebra: cross-sectional Total Psoas Area (TPA), cross-sectional Total Muscle Area (TMA), and Total Parietal Muscle Area (TPMA), defined as TMA without TPA. Measures were obtained for entire muscle surface, as well as by excluding fatty infiltration based on CT attenuation. Findings were stratified for gender, and a threshold of 33rd percentile was set to define sarcopenia. Acute Respiratory Failure (ARF), Acute Respiratory Distress Syndrome (ARDS), and 30-day mortality were assessed as parameters of short-term-outcome., Results: Two hundred thirty-four patients with pneumonectomy (right, n = 107; left, n = 127) were analysed. Postoperative mortality rate was 9.0 % (21/234), 17.1 % of patients (40/234) experienced ARF requiring re-intubation, and 10.3 % (24/234) had ARDS. All parameters describing sarcopenia gave significant results; the best discriminating parameter was TMA after excluding fat (p < 0.001). While right sided pneumonectomy and sarcopenia were independently associated to the three short-term outcome parameters, Charlson Comorbidity Index only independently predicted ARF., Conclusions: Sarcopenia defined as the sex-related 33rd percentile of fat-excluded TMA at the level of the third lumbar vertebra is the most discriminating parameter to assess short-term-outcome in patients undergoing pneumonectomy., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The increasing use of stereotactic body radiation therapy for lung tumours comes along with new post-therapeutic imaging findings that should be known by physicians involved in patient follow-up. Radiation-induced lung injury is much more frequent than after conventional radiation therapy, it can also be delayed and has a different radiological presentation. Radiation-induced lung injury after stereotactic body radiation therapy involves the lung parenchyma surrounding the target tumour and appears as a dynamic process continuing for years after completion of the treatment. Thus, the radiological pattern and the severity of radiation-induced lung injury are prone to changes during follow-up, which can make it difficult to differentiate from local recurrence. Contrary to radiation-induced lung injury, local recurrence after stereotactic body radiation therapy is rare. Other complications mainly depend on tumour location and include airway complications, rib fractures and organizing pneumonia. The aim of this article is to provide a wide overview of radiological changes occurring after SBRT for lung tumours. Awareness of changes following stereotactic body radiation therapy should help avoiding unnecessary interventions for pseudo tumoral presentations., (Copyright © 2020 Société française de radiothérapie oncologique (SFRO). Published by Elsevier Masson SAS. All rights reserved.)

Objectives: To evaluate the diagnostic performance of a deep learning algorithm for automated detection of small 18 F-FDG-avid pulmonary nodules in PET scans, and to assess whether novel block sequential regularized expectation maximization (BSREM) reconstruction affects detection accuracy as compared to ordered subset expectation maximization (OSEM) reconstruction., Methods: Fifty-seven patients with 92 18 F-FDG-avid pulmonary nodules (all ≤ 2 cm) undergoing PET/CT for oncological (re-)staging were retrospectively included and a total of 8824 PET images of the lungs were extracted using OSEM and BSREM reconstruction. Per-slice and per-nodule sensitivity of a deep learning algorithm was assessed, with an expert readout by a radiologist/nuclear medicine physician serving as standard of reference. Receiver-operator characteristic (ROC) curve of OSEM and BSREM were assessed and the areas under the ROC curve (AUC) were compared. A maximum standardized uptake value (SUV max )-based sensitivity analysis and a size-based sensitivity analysis with subgroups defined by nodule size was performed., Results: The AUC of the deep learning algorithm for nodule detection using OSEM reconstruction was 0.796 (CI 95%; 0.772-0.869), and 0.848 (CI 95%; 0.828-0.869) using BSREM reconstruction. The AUC was significantly higher for BSREM compared to OSEM (p = 0.001). On a per-slice analysis, sensitivity and specificity were 66.7% and 79.0% for OSEM, and 69.2% and 84.5% for BSREM. On a per-nodule analysis, the overall sensitivity of OSEM was 81.5% compared to 87.0% for BSREM., Conclusions: Our results suggest that machine learning algorithms may aid detection of small 18 F-FDG-avid pulmonary nodules in clinical PET/CT. AI performed significantly better on images with BSREM than OSEM., Key Points: • The diagnostic value of deep learning for detecting small lung nodules (≤ 2 cm) in PET images using BSREM and OSEM reconstruction was assessed. • BSREM yields higher SUV max of small pulmonary nodules as compared to OSEM reconstruction. • The use of BSREM translates into a higher detectability of small pulmonary nodules in PET images as assessed with artificial intelligence.

Pulmonary nodules - an overview Abstract. Computed tomography (CT) offers fast temporal and high spatial resolution and is increasingly employed for various investigations. Since the 1990s, when multislice computed tomography (CT) technique became commonly available, the detection rate of incidentally detected pulmonary nodules has increased. The aetiology of pulmonary nodules can range from infectious over interstitial lung disease to malignant entities and pose a diagnostic dilemma: Should the incidental finding be dismissed or further investigated? If further investigated which modality and which time frame should be used? Due to the multidisciplinary nature of data required for the complex assessment of an incidental pulmonary nodule, management guidelines are needed in the diagnostic process such as those proposed by the Fleischner Society. The aim of this review is to discuss the different aetiologies pf pulmonary nodules and their potential work-up. Finally, we will also discuss the utility of lung cancer screening.

Purpose: To assess the interreader variability of submillisievert CT for lung cancer screening in radiologists with various experience levels., Method: Six radiologists with different degrees of clinical experience in radiology (range, 1-15 years), rated 100 submillisievert CT chest studies as either negative screening finding (no nodules, benign nodules, nodules <5 mm), indeterminate finding (nodules 5-10 mm), positive finding (nodules >10 mm). Each radiologist interpreted scans randomly ordered and reading time was recorded. Interobserver agreement was assessed with ak statistic. Reasons for differences in nodule classification were analysed on a case-by-case basis. Reading time was correlated with reader experience using Pearson correlation (r)., Results: The overall interobserver agreement between all readers was moderate (k = 0.454; p < 0.001). In 57 patients, all radiologists agreed on the differentiation of negative and indeterminate/positive finding. In 64 cases disagreement between readers led to different nodule classification. In 8 cases some readers rated the nodule as benign, whereas others scored the case as positive. Overall, disagreement in nodule classification was mostly due to failure in identification of target lesion (n = 40), different lesion measurement (n = 44) or different classification (n = 26). Mean overall reading time per scan was of 2 min 2 s (range: 7s-7 min 45 s) and correlated with reader-experience (r = -0.824)., Conclusions: Our study showed substantial interobserver variability for the detection and classification of pulmonary nodules in submillisievert CT. This highlights the importance for careful standardisation of screening programs with the objective of harmonizing efforts of involved radiologists across different institutions by defining and assuring quality standards., (Copyright © 2019. Published by Elsevier B.V.)

Objective: To evaluate whether vessel-suppressed computed tomography (VSCT) can be reliably used for semi-automated volumetric measurements of solid pulmonary nodules, as compared to standard CT (SCT) MATERIAL AND METHODS: Ninety-three SCT were elaborated by dedicated software (ClearRead CT, Riverain Technologies, Miamisburg, OH, USA), that allows subtracting vessels from lung parenchyma. Semi-automated volumetric measurements of 65 solid nodules were compared between SCT and VSCT. The measurements were repeated by two readers. For each solid nodule, volume measured on SCT by Reader 1 and Reader 2 was averaged and the average volume between readers acted as standard of reference value. Concordance between measurements was assessed using Lin's Concordance Correlation Coefficient (CCC). Limits of agreement (LoA) between readers and CT datasets were evaluated., Results: Standard of reference nodule volume ranged from 13 to 366 mm 3 . The mean overestimation between readers was 3 mm 3 and 2.9 mm 3 on SCT and VSCT, respectively. Semi-automated volumetric measurements on VSCT showed substantial agreement with the standard of reference (Lin's CCC = 0.990 for Reader 1; 0.985 for Reader 2). The upper and lower LoA between readers' measurements were (16.3, -22.4 mm 3 ) and (15.5, -21.4 mm 3 ) for SCT and VSCT, respectively., Conclusions: VSCT datasets are feasible for the measurements of solid nodules, showing an almost perfect concordance between readers and with measurements on SCT., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Purpose: Lung cancer screening with CT has been recently recommended for decreasing lung cancer mortality. The radiation dose of CT, however, must be kept as low as reasonably achievable for reducing potential stochastic risks from ionizing radiation. The purpose of this study was to calculate individual patients' lung doses and to estimate cancer risks in low-dose CT (LDCT) in comparison with a standard dose CT (SDCT) protocol., Materials and Methods: This study included 47 adult patients (mean age 63.0 ± 5.7 years) undergoing chest CT on a third-generation dual-source scanner. 23/47 patients (49%) had a non-enhanced chest SDCT, 24 patients (51%) underwent LDCT at 100 kVp with spectral shaping at a dose equivalent to a chest x-ray. 3D-dose distributions were obtained from Monte Carlo simulations for each patient, taking into account their body size and individual CT protocol. Based on the dose distributions, patient-specific lung doses were calculated and relative cancer risk was estimated according to BEIR VII recommendations., Results: As compared to SDCT, the LDCT protocol allowed for significant organ dose and cancer risk reductions (p<0.001). On average, lung dose was reduced from 7.7 mGy to 0.3 mGy when using LDCT, which was associated with lowering of the cancer risk from 8.6 to 0.35 per 100'000 cases. A strong linear correlation between lung dose and patient effective diameter was found for both protocols (R2 = 0.72 and R2 = 0.75 for SDCT and LDCT, respectively)., Conclusion: Use of a LDCT protocol for chest CT with a dose equivalent to a chest x-ray allows for significant lung dose and cancer risk reduction from ionizing radiation.

Purpose: To investigate the diagnostic accuracy of sequential co-registered PET+MR (PET+MR) for local staging of malignant pleural mesothelioma (MPM) compared to PET/CT., Material and Methods: In a prospective clinical trial 34 consecutive patients (median age 66 years; range 40-79 years; 1 female, 33 male) with known MPM, who underwent PET/CT and PET+MR exams for either staging or re-staging/follow-up were evaluated. Imaging was conducted using a tri-modality PET/CT-MR set-up (Discovery PET/CT 690, 3T Discovery MR 750w, both GE Healthcare, Waukesha, WI, USA). In 26 cases histopathology served as standard of reference. Two independent readers evaluated images for T and N stage, confidence level (sure to unsure; 1-3) and subjective overall image quality (very good to non-diagnostic; 1-4). Inter-observer agreement of T and N stages (Cohen's kappa) and interclass correlation coefficient (ICC) between PET/CT vs. PET+MR was calculated., Results: Inter observer agreement for evaluation of T and N Stage in PET/CT images was excellent (k=0.844 and k=0.824, respectively), whereas PET+MR imaging showed substantial agreement in T and N stage (k=0.729 and k=0.691, respectively). The ICC of PET/CT vs. PET+MR for evaluation of both, T and N Stage, was excellent (ICC=0.951 and ICC=0.93, respectively). Diagnostic confidence was scored significantly higher in PET+MR compared to PET/CT (mean score=1.66 and 1.93, respectively; p=0.004). Image quality was diagnostic for all image series. Comparing pT and pN stage vs cT and cN stage (n=26 cases), both imaging modalities showed excellent agreement for T stage (ICCPET+MR=0.888 vs. ICCPET/CT=0.853, respectively) and substantial to moderate agreement for N stage (ICCPET+MR=0.683 vs. ICC=0.595PET/CT, respectively)., Conclusion: Our findings suggest that diagnostic accuracy of PET+MR is comparable to PET/CT for local staging of MPM, whereas radiologists felt significantly more confident staging PET+MR compared to PET/CT images (p=0003), using dedicated sequences., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)