Lumpectomy Specimen Radiography: Does Orientation or 3-Dimensional Tomosynthesis Improve Margin Assessment?

PURPOSE: Our purpose was twofold. First, we sought to determine whether 2 orthogonal oriented views of excised breast cancer specimens could improve surgical margin assessment compared to a single unoriented view. Second, we sought to determine whether 3D tomosynthesis could improve surgical margin assessment compared to 2D mammography alone. MATERIALS AND METHODS: Forty-one consecutive specimens were prospectively imaged using 4 protocols: single view unoriented 2D image acquired on a specimen unit (1VSU), 2 orthogonal oriented 2D images acquired on the specimen unit (2VSU), 2 orthogonal oriented 2D images acquired on a mammogram unit (2V2DMU), and 2 orthogonal oriented 3D images acquired on the mammogram unit (2V3DMU). Three breast imagers randomly assessed surgical margin of the 41 specimens with each protocol. Surgical margin per histopathology was considered the gold standard. RESULTS: The average area under the curve (AUC) was 0.60 for 1VSU, 0.66 for 2VSU, 0.68 for 2V2DMU, and 0.60 for 2V3DMU. Comparing AUCs for 2VSU vs 1VSU by reader showed improved diagnostic accuracy using 2VSU; however, this difference was only statistically significant for reader 3 (0.73 vs 0.63, P = .0455). Comparing AUCs for 2V3DMU vs 2V2DMU by reader showed mixed results, with reader 1 demonstrating increased accuracy (0.72 vs 0.68, P = .5984), while readers 2 and 3 demonstrated decreased accuracy (0.50 vs 0.62, P = .1089 and 0.58 vs 0.75, P = .0269). CONCLUSIONS: 2VSU showed improved accuracy in surgical margin prediction compared to 1VSU, although this was not statistically significant for all readers. 3D tomosynthesis did not improve surgical margin assessment.

Feasibility of Perioperative Micro-Computed Tomography of Human Lung Cancer Specimens: A Pilot Study.

CONTEXT.­: Lesion localization during intraoperative frozen section of lung resection specimens can be challenging. Imaging could aid lesion localization while enabling 3-dimensional specimen analysis. OBJECTIVE.­: To assess the feasibility of integrating micro-computed tomography (micro-CT) into the perioperative evaluation of fresh surgical lung resection specimens. DESIGN.­: Fresh lung specimens from patients with a presumptive diagnosis of lung cancer were imaged with micro-CT prior to routine histopathologic and molecular analysis. Micro-CT images were assessed to determine image quality, lesion size, and distance from lesion to the nearest surgical margin. Micro-CT measurements were compared to pathologic measurements using Bland-Altman analysis. RESULTS.­: A total of 22 specimens from 21 patients were analyzed (mean image acquisition time, 13 ± 6 minutes). Histologic quality of imaged specimens was indistinguishable from a control group of nonimaged lung specimens. Artifacts, most commonly from specimen deflation (n = 8), obscured fine detail on micro-CT images of 10 specimens. Micro-CT could successfully localize the target lesion in the other 12 specimens. Distance to the nearest surgical margin was determined in 10 specimens. Agreement of micro-CT with final pathology was good, with a mean difference of -2.8% (limits of agreement -14.5% to 20.0%) for lesion size and -0.5 mm (limits of agreement -4.4 to 3.4 mm) for distance to nearest surgical margin. CONCLUSIONS.­: Micro-CT of fresh surgical lung specimens is feasible and has the potential to evaluate the size and location of lesions within resection specimens, as well as distance to the nearest surgical margin, all without compromising specimen integrity.

Thoracic Skeletal Muscle Is Associated With Adverse Outcomes After Lobectomy for Lung Cancer.

BACKGROUND: Assessment of risk associated with lung cancer resection is primarily based on evaluation of cardiopulmonary function and remains imprecise. We investigated the relationship between thoracic muscle and early outcomes after lobectomy. METHODS: Cross-sectional area of skeletal muscle was measured at the level of the fifth thoracic vertebra on computed tomography in 135 consecutive patients before lobectomy for lung cancer. Patients were stratified into low and high muscle groups using the sex-specific muscle median. Primary outcome was a composite of any postoperative complication as per The Society of Thoracic Surgeons General Thoracic Surgical Database. Secondary outcomes included postoperative respiratory complications, postoperative intensive care unit admission, hospital length of stay, and hospital readmission within 30 days of hospital discharge. The χ2 test, adjusted multivariable regression analysis, and likelihood ratio test were performed. RESULTS: Patients with low muscle were significantly more likely to have any postoperative complication and respiratory postoperative complications. Although postoperative intensive care unit admission was similar for low muscle and high muscle groups, low muscle patients had longer hospital length of stay and a higher rate of hospital readmission. Adjusted multivariable regression revealed the independent association of thoracic muscle with all outcomes. The likelihood ratio test suggested that thoracic muscle adds predictive capability to information captured by preoperative pulmonary function testing. CONCLUSIONS: Low thoracic muscle is independently associated with increased postoperative complications and health care utilization among patients undergoing lobectomy for lung cancer. Evaluation of thoracic muscle may enhance risk prediction models.

Quantifying the effect of slice thickness, intravenous contrast and tube current on muscle segmentation: Implications for body composition analysis.

OBJECTIVES: To quantify the effect of IV contrast, tube current and slice thickness on skeletal muscle cross-sectional area (CSA) and density (SMD) on routine CT. METHODS: CSA and SMD were computed on 216 axial CT images obtained at the L3 level in 72 patients with variations in IV contrast, slice thickness and tube current. Intra-patient mean difference (MD), 95 % CI and limits of agreement were calculated using the Bland-Altman approach. Inter- and intra-analyst agreement was evaluated. RESULTS: IV contrast significantly increased CSA by 1.88 % (MD 2.33 cm2; 95 % CI 1.76-2.89) and SMD by 5.99 % (p<0.0001). Five mm slice thickness significantly increased mean CSA by 1.11 % compared to 2 mm images (1.32 cm2; 0.78-1.85) and significantly decreased SMD by 11.64 % (p<0.0001). Low tube current significantly decreased mean CSA by 4.79 % (6.44 cm2; 3.78-9.10) and significantly increased SMD by 46.46 % (p<0.0001). Inter- and intra-analyst agreement was excellent. CONCLUSIONS: IV contrast, slice thickness and tube current significantly affect CSA and SMD. Investigators designing and analysing clinical trials using CT for body composition analysis should report CT acquisition parameters and consider the effect of slice thickness, IV contrast and tube current on myometric data. KEY POINTS: • Intravenous contrast, slice thickness and tube current significantly affect myometric data. • Image acquisition parameter variations may obscure intrapatient muscle differences on serial measurements. • Investigators using CT for body composition analysis should report CT acquisition parameters.

Sarcopenia Is Associated with Quality of Life and Depression in Patients with Advanced Cancer.

BACKGROUND: Patients with advanced cancer often experience muscle wasting (sarcopenia), yet little is known about the characteristics associated with sarcopenia and the relationship between sarcopenia and patients' quality of life (QOL) and mood. MATERIALS AND METHODS: As part of a randomized trial, we assessed baseline QOL (Functional Assessment of Cancer Therapy-General [FACT-G]) and mood (Hospital Anxiety and Depression Scale [HADS]) in patients within 8 weeks of diagnosis of incurable lung or gastrointestinal cancer, and prior to randomization. Using computed tomography scans collected as part of routine clinical care, we assessed sarcopenia at the level of the third lumbar vertebra with validated sex-specific cutoffs. We used logistic regression to explore characteristics associated with presence of sarcopenia. To examine associations between sarcopenia, QOL and mood, we used linear regression, adjusted for patients' age, sex, marital status, education, and cancer type. RESULTS: Of 237 participants (mean age = 64.41 ± 10.93 years), the majority were male (54.0%) and married (70.5%) and had lung cancer (56.5%). Over half had sarcopenia (55.3%). Older age (odds ratio [OR] = 1.05, p = .002) and education beyond high school (OR = 1.95, p = .047) were associated with greater likelihood of having sarcopenia, while female sex (OR = 0.25, p < .001) and higher body mass index (OR = 0.79, p < .001) correlated with lower likelihood of sarcopenia. Sarcopenia was associated with worse QOL (FACT-G: B = -4.26, p = .048) and greater depression symptoms (HADS-depression: B = -1.56, p = .005). CONCLUSION: Sarcopenia was highly prevalent among patients with newly diagnosed, incurable cancer. The associations of sarcopenia with worse QOL and depression symptoms highlight the need to address the issue of sarcopenia early in the course of illness. IMPLICATIONS FOR PRACTICE: This study found that sarcopenia, assessed using computed tomography scans acquired as part of routine clinical care, is highly prevalent in patients with newly diagnosed, incurable cancer. Notably, patients with sarcopenia reported worse quality of life and greater depression symptoms than those without sarcopenia. These findings highlight the importance of addressing muscle loss early in the course of illness among patients with incurable cancer. In the future, investigators should expand upon these findings to develop strategies for assessing and treating sarcopenia while striving to enhance the quality of life and mood outcomes of patients with advanced cancer.

Lumbar skeletal muscle index derived from routine computed tomography exams predict adverse post-extubation outcomes in critically ill patients.

PURPOSE: To evaluate the effect of a skeletal muscle index derived from a routine CT image at the level of vertebral body L3 (L3SMI) on outcomes of extubated patients in the surgical intensive care unit. MATERIALS AND METHODS: 231 patients of a prospective observational trial (NCT01967056) who had undergone CT within 5days of extubation were included. L3SMI was computed using semi-automated segmentation. Primary outcomes were pneumonia within 30days of extubation, adverse discharge disposition and 30-day mortality. Secondary outcomes included re-intubation within 72h, total hospital costs, ICU length of stay (LOS), post-extubation LOS and total hospital LOS. Outcomes were analyzed using multivariable regression models with a priori-defined covariates height, gender, age, APACHE II score and Charlson Comorbidity Index. RESULTS: L3SMI was an independent predictor of pneumonia (aOR 0.96; 95% CI 0.941-0.986; P=0.002), adverse discharge disposition (aOR 0.98; 95% CI 0.957-0.999; P=0.044) and 30-day mortality (aOR 0.94; 95% CI 0.890-0.995; P=0.033). L3SMI was significantly lower in re-intubated patients (P=0.024). Secondary analyses suggest that L3SMI is associated with total hospital costs (P=0.043) and LOS post-extubation (P=0.048). CONCLUSION: The lumbar skeletal muscle index, derived from routine abdominal CT, is an objective prognostic tool at the time of extubation.

Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis.

Pretreatment risk stratification is key for personalized medicine. While many physicians rely on an "eyeball test" to assess whether patients will tolerate major surgery or chemotherapy, "eyeballing" is inherently subjective and difficult to quantify. The concept of morphometric age derived from cross-sectional imaging has been found to correlate well with outcomes such as length of stay, morbidity, and mortality. However, the determination of the morphometric age is time intensive and requires highly trained experts. In this study, we propose a fully automated deep learning system for the segmentation of skeletal muscle cross-sectional area (CSA) on an axial computed tomography image taken at the third lumbar vertebra. We utilized a fully automated deep segmentation model derived from an extended implementation of a fully convolutional network with weight initialization of an ImageNet pre-trained model, followed by post processing to eliminate intramuscular fat for a more accurate analysis. This experiment was conducted by varying window level (WL), window width (WW), and bit resolutions in order to better understand the effects of the parameters on the model performance. Our best model, fine-tuned on 250 training images and ground truth labels, achieves 0.93 ± 0.02 Dice similarity coefficient (DSC) and 3.68 ± 2.29% difference between predicted and ground truth muscle CSA on 150 held-out test cases. Ultimately, the fully automated segmentation system can be embedded into the clinical environment to accelerate the quantification of muscle and expanded to volume analysis of 3D datasets.