Medical, ethical, and legal aspects of end-of-life dilemmas in the intensive care unit.

Physicians in the intensive care unit face a myriad of ethical dilemmas involving end-of-life care, yet they receive only minimal training about their jurisprudential obligations, and misconceptions about legal responsibilities abound. In particular, significant uncertainty exists among critical care physicians as to ethical and legal obligations for terminally ill patients. This paper presents 3 hypothetical cases to elucidate the medical, ethical, and legal considerations in common end-of-life situations encountered in the intensive care unit.

Author Correction: Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas.

Purpose To evaluate ability of radiomic (computer-extracted imaging) features to distinguish non-small cell lung cancer adenocarcinomas from granulomas at noncontrast CT. Materials and Methods For this retrospective study, screening or standard diagnostic noncontrast CT images were collected for 290 patients (mean age, 68 years; range, 18-92 years; 125 men [mean age, 67 years; range, 18-90 years] and 165 women [mean age, 68 years; range, 33-92 years]) from two institutions between 2007 and 2013. Histopathologic analysis was available for one nodule per patient. Corresponding nodule of interest was identified on axial CT images by a radiologist with manual annotation. Nodule shape, wavelet (Gabor), and texture-based (Haralick and Laws energy) features were extracted from intra- and perinodular regions. Features were pruned to train machine learning classifiers with 145 patients. In a test set of 145 patients, classifier results were compared against a convolutional neural network (CNN) and diagnostic readings of two radiologists. Results Support vector machine classifier with intranodular radiomic features achieved an area under the receiver operating characteristic curve (AUC) of 0.75 on the test set. Combining radiomics of intranodular with perinodular regions improved the AUC to 0.80. On the same test set, CNN resulted in an AUC of 0.76. Radiologist readers achieved AUCs of 0.61 and 0.60, respectively. Conclusion Radiomic features from intranodular and perinodular regions of nodules can distinguish non-small cell lung cancer adenocarcinomas from benign granulomas at noncontrast CT. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Nishino in this issue.

Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas.

Adenocarcinomas and active granulomas can both have a spiculated appearance on computed tomography (CT) and both are often fluorodeoxyglucose (FDG) avid on positron emission tomography (PET) scan, making them difficult to distinguish. Consequently, patients with benign granulomas are often subjected to invasive surgical biopsies or resections. In this study, quantitative vessel tortuosity (QVT), a novel CT imaging biomarker to distinguish between benign granulomas and adenocarcinomas on routine non-contrast lung CT scans is introduced. Our study comprised of CT scans of 290 patients from two different institutions, one cohort for training (N = 145) and the other (N = 145) for independent validation. In conjunction with a machine learning classifier, the top informative and stable QVT features yielded an area under receiver operating characteristic curve (ROC AUC) of 0.85 in the independent validation set. On the same cohort, the corresponding AUCs for two human experts including a radiologist and a pulmonologist were found to be 0.61 and 0.60, respectively. QVT features also outperformed well known shape and textural radiomic features which had a maximum AUC of 0.73 (p-value = 0.002), as well as features learned using a convolutional neural network AUC = 0.76 (p-value = 0.028). Our results suggest that QVT features could potentially serve as a non-invasive imaging biomarker to distinguish granulomas from adenocarcinomas on non-contrast CT scans.

Massive hemorrhage after inspection bronchoscopy for carcinoid tumor.

Carcinoid tumor is a neuroendocrine tumor that can arise in the bronchial tree and can be hypervascular. Here we describe a case of bronchial carcinoid tumor in a 34-year-old previously healthy male who presented with hemoptysis and right lung mass. Inspection bronchoscopy revealed bronchus intermedius endobronchial lesion and was complicated by urgent intubation and placement of endobronchial blocker for massive hemorrhage. Subsequent angiography with embolization of the bronchial artery supplying the mass resulted in control of bleeding. While massive hemorrhage has been described with biopsy of bronchial carcinoid tumor, this case suggests that careful planning for inspection bronchoscopy is needed when carcinoid tumor is suspected.

Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography.

Differentiation between benign and malignant nodules is a problem encountered by radiologists when visualizing computed tomography (CT) scans. Adenocarcinomas and granulomas have a characteristic spiculated appearance and may be fluorodeoxyglucose avid, making them difficult to distinguish for human readers. In this retrospective study, we aimed to evaluate whether a combination of radiomic texture and shape features from noncontrast CT scans can enable discrimination between granulomas and adenocarcinomas. Our study is composed of CT scans of 195 patients from two institutions, one cohort for training ([Formula: see text]) and the other ([Formula: see text]) for independent validation. A set of 645 three-dimensional texture and 24 shape features were extracted from CT scans in the training cohort. Feature selection was employed to identify the most informative features using this set. The top ranked features were also assessed in terms of their stability and reproducibility across the training and testing cohorts and between scans of different slice thickness. Three different classifiers were constructed using the top ranked features identified from the training set. These classifiers were then validated on the test set and the best classifier (support vector machine) yielded an area under the receiver operating characteristic curve of 77.8%.

Toxicity risk from glucocorticoids in sarcoidosis patients.

BACKGROUND: Glucocorticoids (GC) are considered first-line therapy for treating sarcoidosis, but there are few data about the adverse consequences of GC. Although there are several steroid-sparing medications available for treatment, a large proportion of patients are treated with prolonged courses of GC. The toxicities of GC in sarcoidosis populations have not been carefully evaluated. METHODS: We performed a retrospective cohort study of all newly diagnosed sarcoidosis patients who had the entirety of their medical care in a single health system. We analyzed the time to development of a composite toxicity end-point, including diabetes, hypertension, weight gain, hyperlipidemia, low bone density and ocular complications of GC using Cox proportional hazards analysis. RESULTS: One hundred and five patients were ever treated with GC, whereas 49 were not treated during a median follow-up of 101 months. GC-treated patients developed 1.3 ± 1.1 toxicities during therapy, versus 0.6 ± 1.0 in the non-treated group. After adjustment for age, gender, race and preexisting conditions, the hazard ratio for ever-treated patients was 2.37 (1.34-4.17) for the composite end-point. Age and the presence of preexisting conditions also were associated with reaching the end-point. Similar effects were seen when analyzed for cumulative GC dose and for duration of GC use. For individual end-points, weight gain (HR 2.04) and new hypertension (HR 3.36) were associated with any use of GC. CONCLUSIONS: Our data suggest that GC are associated with clinically important toxicities in sarcoidosis patients, associated with both the cumulative dose and duration of treatment.

Co-registration of pre-operative CT with ex vivo surgically excised ground glass nodules to define spatial extent of invasive adenocarcinoma on in vivo imaging: a proof-of-concept study.

OBJECTIVE: To develop an approach for radiology-pathology fusion of ex vivo histology of surgically excised pulmonary nodules with pre-operative CT, to radiologically map spatial extent of the invasive adenocarcinomatous component of the nodule. METHODS: Six subjects (age: 75 ± 11 years) with pre-operative CT and surgically excised ground-glass nodules (size: 22.5 ± 5.1 mm) with a significant invasive adenocarcinomatous component (>5 mm) were included. The pathologist outlined disease extent on digitized histology specimens; two radiologists and a pulmonary critical care physician delineated the entire nodule on CT (in-plane resolution: <0.8 mm, inter-slice distance: 1-5 mm). We introduced a novel reconstruction approach to localize histology slices in 3D relative to each other while using CT scan as spatial constraint. This enabled the spatial mapping of the extent of tumour invasion from histology onto CT. RESULTS: Good overlap of the 3D reconstructed histology and the nodule outlined on CT was observed (65.9 ± 5.2%). Reduction in 3D misalignment of corresponding anatomical landmarks on histology and CT was observed (1.97 ± 0.42 mm). Moreover, the CT attenuation (HU) distributions were different when comparing invasive and in situ regions. CONCLUSION: This proof-of-concept study suggests that our fusion method can enable the spatial mapping of the invasive adenocarcinomatous component from 2D histology slices onto in vivo CT. KEY POINTS: • 3D reconstructions are generated from 2D histology specimens of ground glass nodules. • The reconstruction methodology used pre-operative in vivo CT as 3D spatial constraint. • The methodology maps adenocarcinoma extent from digitized histology onto in vivo CT. • The methodology potentially facilitates the discovery of CT signature of invasive adenocarcinoma.