Guidelines for multimodality radiological staging of lung cancer

Dual-Energy CT, Virtual Non-Calcium Bone Marrow Imaging of the Spine: An AI-Assisted, Volumetric Evaluation of a Reference Cohort with 500 CT Scans

Virtual non-calcium (VNCa) images from dual-energy computed tomography (DECT) have shown high potential to diagnose bone marrow disease of the spine, which is frequently disguised by dense trabecular bone on conventional CT. In this study, we aimed to define reference values for VNCa bone marrow images of the spine in a large-scale cohort of healthy individuals. DECT was performed after resection of a malignant skin tumor without evidence of metastatic disease. Image analysis was fully automated and did not require specific user interaction. The thoracolumbar spine was segmented by a pretrained convolutional neuronal network. Volumetric VNCa data of the spine’s bone marrow space were processed using the maximum, medium, and low calcium suppression indices. Histograms of VNCa attenuation were created for each exam and suppression setting. We included 500 exams of 168 individuals (88 female, patient age 61.0 ± 15.9). A total of 8298 vertebrae were segmented. The attenuation histograms’ overlap of two consecutive exams, as a measure for intraindividual consistency, yielded a median of 0.93 (IQR: 0.88–0.96). As our main result, we provide the age- and sex-specific bone marrow attenuation profiles of a large-scale cohort of individuals with healthy trabecular bone structure as a reference for future studies. We conclude that artificial-intelligence-supported, fully automated volumetric assessment is an intraindividually robust method to image the spine’s bone marrow using VNCa data from DECT.

Expression of fibroblast activation protein in lung cancer and its correlation with tumor glucose metabolism and histopathology

PURPOSE

To explore the expression of fibroblast activation protein (FAP) in lung cancer (LC) and its correlation with tumor glucose metabolism and histopathology.

METHODS

From June 2018 to November 2020, 73 patients with newly diagnosed LC were included. Immunohistochemical staining was used to quantify FAP expression in tumors. The histopathological type and tumor grade were determined via histopathological examination. The tumor glucose metabolism parameters and tumor maximal diameter were measured via [¹⁸F] F-FDG PET/CT. Univariate and multivariate analysis were performed to study the correlation of FAP expression levels with glucose metabolism variables and tumor histopathology.

RESULTS

Positive FAP expression was observed in 97.3% (71/73) LC lesions, which was significantly higher than 87.7% (64/73) of [¹⁸F] F-FDG positivity observed on PET/CT (χ² = 4.818, P = 0.028). In 12 early adenocarcinomas (ADCs), only three lesions (25%) were positive for [¹⁸F] F-FDG on PET/CT; however, 10 lesions (83.3%) were positive for FAP. When FAP expression was classified into low level (scores ≤ 3) and high level (scores > 4), high FAP level was found in 80.8% tumors and low FAP level in the other 19.2% tumors. High FAP level was identified in 100.0% of squamous cell carcinomas (SCCs), 85.7% of ADCs, 66.7% (4/6) of large cell neuroendocrine carcinomas (LCNCs), and 40.0% (4/10) of small cell lung cancers (SCLCs) (P < 0.05). In non-mucinous ADC lesions, on univariate analysis, FAP expression level showed a close relationship with tumor metabolism parameters (maximal standard uptake value (SUVmax), mean standard uptake value (SUVmean), and total lesion glycolysis (TLG)), tumor diameter, tumor grade, and lesion attenuation (P < 0.05).

CONCLUSION

The present study demonstrates that FAP is widely expressed in LC and shows great variation in different histopathological types. A high positive rate of FAP expression implies that FAP-targeted imaging may be a sensitive modality for diagnosing LC, especially in early ADCs. Further validation with such probes is warranted.

Alternative Multidisciplinary Management Options for Locally Advanced NSCLC During the Coronavirus Disease 2019 Global Pandemic

The coronavirus disease 2019 (COVID-19) pandemic is currently accelerating. Patients with locally advanced NSCLC (LA-NSCLC) may require treatment in locations where resources are limited, and the prevalence of infection is high. Patients with LA-NSCLC frequently present with comorbidities that increase the risk of severe morbidity and mortality from COVID-19. These risks may be further increased by treatments for LA-NSCLC. Although guiding data is scarce, we present an expert thoracic oncology multidisciplinary (radiation oncology, medical oncology, surgical oncology) consensus of alternative strategies for the treatment of LA-NSCLC during a pandemic. The overarching goals of these approaches are the following: (1) reduce the number of visits to a health care facility, (2) reduce the risk of exposure to severe acute respiratory syndrome-coronavirus-2, (3) attenuate the immunocompromising effects of lung cancer therapies, and (4) provide effective oncologic therapy. Patients with resectable disease can be treated with definitive nonoperative management if surgical resources are limited or the risks of perioperative care are high. Nonoperative options include chemotherapy, chemoimmunotherapy, and radiation therapy with sequential schedules that may or may not affect long-term outcomes in an era in which immunotherapy is available. The order of treatments may be on the basis of patient factors and clinical resources. Whenever radiation therapy is delivered without concurrent chemotherapy, hypofractionated schedules are appropriate. For patients who are confirmed to have COVID-19, usually, cancer therapies may be withheld until symptoms have resolved with negative viral test results. The risk of severe treatment-related morbidity and mortality is increased for patients undergoing treatment for LA-NSCLC during the COVID-19 pandemic. Adapting alternative treatment strategies as quickly as possible may save lives and should be implemented through communication with the multidisciplinary cancer team.

Automatic Detection and Staging of Lung Tumors using Locational Features and Double-Staged Classifications

Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any cancer worldwide. Clinical staging of lung cancer can significantly reduce the mortality rate, because effective treatment options strongly depend on the specific stage of cancer. Unfortunately, manual staging remains a challenge due to the intensive effort required. This paper presents a computer-aided diagnosis (CAD) method for detecting and staging lung cancer from computed tomography (CT) images. This CAD works in three fundamental phases: segmentation, detection, and staging. In the first phase, lung anatomical structures from the input tomography scans are segmented using gray-level thresholding. In the second, the tumor nodules inside the lungs are detected using some extracted features from the segmented tumor candidates. In the last phase, the clinical stages of the detected tumors are defined by extracting locational features. For accurate and robust predictions, our CAD applies a double-staged classification: the first is for the detection of tumors and the second is for staging. In both classification stages, five alternative classifiers, namely the Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Ensemble Tree (ET), and Back Propagation Neural Network (BPNN), are applied and compared to ensure high classification performance. The average accuracy levels of 92.8% for detection and 90.6% for staging are achieved using BPNN. Experimental findings reveal that the proposed CAD method provides preferable results compared to previous methods; thus, it is applicable as a clinical diagnostic tool for lung cancer.

Automated lung volumetry from routine thoracic CT scans: how reliable is the result?

RATIONALE AND OBJECTIVES

Today, lung volumes can be easily calculated from chest computed tomography (CT) scans. Modern postprocessing workstations allow automated volume measurement of data sets acquired. However, there are challenges in the use of lung volume as an indicator of pulmonary disease when it is obtained from routine CT. Intra-individual variation and methodologic aspects have to be considered. Our goal was to assess the reliability of volumetric measurements in routine CT lung scans.

MATERIALS AND METHODS

Forty adult cancer patients whose lungs were unaffected by the disease underwent routine chest CT scans in 3-month intervals, resulting in a total number of 302 chest CT scans. Lung volume was calculated by automatic volumetry software. On average of 7.2 CT scans were successfully evaluable per patient (range 2-15). Intra-individual changes were assessed.

RESULTS

In the set of patients investigated, lung volume was approximately normally distributed, with a mean of 5283 cm(3) (standard deviation = 947 cm(3), skewness = -0.34, and curtosis = 0.16). Between different scans in one and the same patient the median intra-individual standard deviation in lung volume was 853 cm(3) (16% of the mean lung volume).

CONCLUSIONS

Automatic lung segmentation of routine chest CT scans allows a technically stable estimation of lung volume. However, substantial intra-individual variations have to be considered. A median intra-individual deviation of 16% in lung volume between different routine scans was found.