Dual-energy computed tomography for evaluating nodal staging in lung adenocarcinoma: correlation with surgical pathology.

PURPOSE: To ascertain the performance of dual-energy CT (DECT) with iodine quantification in differentiating malignant mediastinal and hilar lymph nodes (LNs) from benign ones, focusing on patients with lung adenocarcinoma. MATERIALS AND METHODS: In this study, patients with suspected lung cancer received a preoperative contrast-enhanced DECT scan from Jun 2018 to Dec 2020. Quantitative DECT parameters and the size were compared between metastatic and benign LNs. Their diagnostic performances were analyzed by the ROC curves and compared by using the two-sample t test. RESULTS: 72 patients (23 men, 49 women; mean age 62.5 ± 10.1 years) fulfilled the inclusion criteria. A total of 98 LNs (67 benign, 31 metastatic) were analyzed. The iodine concentration normalized by muscle (NICmuscle) was significantly higher (P < 0.001) in metastatic LNs (4.79 ± 1.70) than in benign ones (3.00 ± 1.45). The optimal threshold of NICmuscle was 3.44, which yielded AUC: 0.798, sensitivity: 83.9%, specificity: 73.1%, accuracy: 76.5%, respectively. Applying the established size parameters with 10 mm as the threshold yielded AUC: 0.600, sensitivity: 29.0%, specificity: 91.0%, accuracy: 71.4%, respectively. The diagnostic performance of NICmuscle was significantly better (P = 0.007) than the performance obtained using the established size parameters. CONCLUSIONS: For lung adenocarcinoma, the quantitative measurement of NICmuscle derived from DECT is useful for differentiating benign and metastatic mediastinal and hilar LNs before surgical intervention.

Multi-energy level fusion for nodal metastasis classification of primary lung tumor on dual energy CT using deep learning.

Lymph node metastasis also called nodal metastasis (Nmet), is a clinically primary task for physicians. The survival and recurrence of lung cancer are related to the Nmet staging from Tumor-Node-Metastasis (TNM) reports. Furthermore, preoperative Nmet prediction is still a challenge for the patient in managing the surgical plan and making treatment decisions. We proposed a multi-energy level fusion model with a principal feature enhancement (PFE) block incorporating radiologist and computer science knowledge for Nmet prediction. The proposed model is custom-designed by gemstone spectral imaging (GSI) with different energy levels on dual-energy computer tomography (CT) from a primary tumor of lung cancer. In the experiment, we take three different energy level fusion datasets: lower energy level fusion (40, 50, 60, 70 keV), higher energy level fusion (110, 120, 130, 140 keV), and average energy level fusion (40, 70, 100, 140 keV). The proposed model is trained by lower energy level fusion that is 93% accurate and the value of Kappa is 86%. When we used the lower energy level images to train the fusion model, there has been a significant difference to other energy level fusion models. Hence, we apply 5-fold cross-validation, which is used to validate the performance result of the multi-keV model with different fusion datasets of energy level images in the pathology report. The cross-validation result also demonstrates that the model with the lower energy level dataset is more robust and suitable in predicting the Nmet of the primary tumor. The lower energy level shows more information of tumor angiogenesis or heterogeneity provided the proposed fusion model with a PFE block and channel attention blocks to predict Nmet from primary tumors.

Dual energy CT image prediction on primary tumor of lung cancer for nodal metastasis using deep learning.

Lymph node metastasis (LNM) identification is the most clinically important tasks related to survival and recurrence from lung cancer. However, the preoperative prediction of nodal metastasis remains a challenge to determine surgical plans and pretreatment decisions in patients with cancers. We proposed a novel deep prediction method with a size-related damper block for nodal metastasis (Nmet) identification from the primary tumor in lung cancer generated by gemstone spectral imaging (GSI) dual-energy computer tomography (CT). The best model is the proposed method trained by the 40 keV dataset achieves an accuracy of 86 % and a Kappa value of 72 % for Nmet prediction. In the experiment, we have 11 different monochromatic images from 40∼140 keV (the interval is 10 keV) for each patient. When we used the model of 40 keV dataset, there has significant difference in other energy levels (unit of keV). Therefore, we apply in 5-fold cross-validation to explain the lower keV is more efficient to predict Nmet of the primary tumor. The result shows that tumor heterogeneity and size contributed to the proposed model to estimate whether absence or presence of nodal metastasis from the primary tumor.

Three-dimensional nucleus-to-cytoplasm ratios provide better discrimination of normal and lung adenocarcinoma cells than in two dimensions.

We acquired multiphoton images of normal and lung adenocarcinoma cell lines in three dimensions. Image stacks of the cells were then processed to obtain nucleus-to-cytoplasm (N/C) ratios in two and three dimensions. While N/C ratios in three dimensions can be unambiguously determined from the volumetric ratios of the nucleus and cytoplasm, two-dimensional (2-D) N/C can vary depending on the axial plane selected for N/C ratio determination. We determined 2-D N/C ratios from three criteria: (1) axial position at which the nuclear area is the largest; (2) the largest 2-D N/C ratio value; and (3) axial position at the midpoint of nuclear axial position. We found that different definitions of 2-D N/C ratio will significantly affect its value. Furthermore, in general, larger variance was found in 2-D rather than three-dimensional (3-D) N/C ratios. Lack of ambiguity in definition and reduced variance suggest that 3-D N/C ratio is a better parameter for characterizing tumor cells in the clinical setting.

A machine learning approach for predicting urine output after fluid administration.

BACKGROUND AND OBJECTIVE: To develop a machine learning model to predict urine output (UO) in sepsis patients after fluid resuscitation. METHODS: We identified sepsis patients in the Multiparameter Intelligent Monitoring in Intensive Care-III v1.4 database according to the Sepsis-3 criteria. We focused on two outcomes: whether the UO decreased after fluid administration and whether oliguria (defined as UO less than the threshold of 0.5 mL/kg/h) developed. A gradient tree-based machine learning model implemented with an eXtreme Gradient Boosting algorithm was used to integrate relevant physiological parameters for predicting the aforementioned outcomes. A confusion matrix was computed. RESULTS: A total of 232,929 events in 19,275 patients were included. Using decreased UO as the outcome measure, the optimal model achieved an area under the curve (AUC) of 0.86; for predicting oliguria, most models achieved an AUC greater than 0.86, and the highest sensitivity was 92.2% when the model was applied to patients with baseline oliguria. CONCLUSIONS: Machine learning could help clinicians evaluate fluid status in sepsis patients after fluid administration, thus preventing fluid overload-related complications.

Detection of Lumbar Spine Osseous Metastases Using Dual-Energy CT: Phantom Results and Preliminary Clinical Validation.

OBJECTIVE: The purpose of this study was to evaluate the sensitivity, tumor conspicuity, and image quality of different material decomposition images of phantoms and patients with nearly isodense bone metastases using rapid-kilovoltage-switching dual-energy CT (DECT). MATERIALS AND METHODS: Fifty-one semianthropomorphic lumbar spine phantoms embedded with 75 simulated tumors were scanned without and with outer torso-attenuating encasement under the same scan settings. Two radiologists independently reviewed the 70-keV virtual monochromatic and material decomposition images (hydroxyapatite-water, water-hydroxyapatite, cortical bone-water, water-cortical bone). The sensitivity of tumor detection, tumor conspicuity (on a 3-point scale), and image quality (on a 3-point scale) were recorded by two independent readers. McNemar and Wilcoxon signed rank tests were used to compare results between the image reconstructions. Six clinical abdominopelvic DECT scans (three men, three women; mean age, 52 years) with nine nearly isodense lumbar spine tumors missed in the clinical report but confirmed on other scans were also evaluated. RESULTS: The hydroxyapatite-water material decomposition algorithm showed improved sensitivity for isodense lesion detection (without torso phantom encasement, 94% vs 82%, p = 0.031; with torso phantom encasement, 38% vs 18%, p = 0.013), and higher tumor conspicuity scores (p < 0.0001) compared with 70-keV virtual monoenergetic images. Artifacts were more prevalent with all material decomposition images than with 70-keV virtual monoenergetic images. Similar results were seen in the patient study. CONCLUSION: Dual-energy CT with hydroxyapatite-water material decomposition may improve the detection of bone marrow metastases, especially for subtle isodense tumors. Further study in prospective clinical scans is warranted.

The Relationship Between Hematoma and Pachymeninges in an Interdural Hematoma: Diagnosis and Surgical Strategy.

BACKGROUND: The exact location of a hematoma in relation to the pachymeninges contributes to typical radiographic presentations. However, because of the complexity of hematoma evolution and neomembrane formation, an unexpected intraoperative finding can lead to a change of surgical strategy. In addition, the concentration of hemoglobin and its degradation products, the integrity of red blood cells, and the infiltration of fibroblasts, polymorphonuclear neutrophils, and macrophages are factors that affect the imaging characteristics on computed tomography and magnetic resonance imaging as the hematoma ages. CASE DESCRIPTION: An interdural hematoma (IDH)-a hemorrhage that splits the periosteal dura mater from the meningeal dura mater-is an extremely rare occurrence, and the diagnosis requires confirmation by surgical or pathologic findings. By presenting a case of an IDH that was misdiagnosed as a chronic subdural hematoma before surgery, and reviewing the literature, we propose the radiologic characteristics of presenting both dural border sign and dural beak sign on magnetic resonance imaging as a specific indicator for IDH preoperatively. CONCLUSIONS: A careful evaluation of cerebral expansion before membranectomy was mandatory intraoperatively. For IDH, wide inner membranectomy (i.e., excision of meningeal dura mater) should not be necessary. An IDH should be considered as a distinct disease category when evaluating an extra-axial hematoma despite its rarity, because the characters of radiologic, histopathologic findings are different. In addition, surgical strategy varies for epidural or subdural hematoma in different hematoma stages.

Clinical feasibility of Gd-EOB-DTPA-enhanced MR imaging for assessing liver function: validation with ICG tests and parenchymal cell volume.

OBJECTIVE: Using Gd-EOB-DTPA-enhanced MRI to assess liver function and validate with indocyanine green (ICG) tests and parenchymal cell volume (PCV). METHODS: Nineteen patients scheduled for operation were enrolled. Functional liver volume (FLV) was computed from 20min post-contrast images. ICG retention was measured in serum 15min after injection. The histological PCV was determined from resected liver specimen. RESULTS: FLV showed significantly correlated with ICG retention (ICG-R15: r=-0.47, P=.042) and PCV (r=0.814, P<.001). CONCLUSIONS: Gd-EOB-DTPA-enhanced MRI is clinically feasible for quantifying liver function.

Si⁺-implanted Si-wire waveguide photodetectors for the mid-infrared.

CMOS-compatible Si⁺-implanted Si-waveguide p-i-n photodetectors operating at room temperature and at mid-infrared wavelengths from 2.2 to 2.3 µm are demonstrated. Responsivities of 9.9 ± 2.0 mA/W are measured at a 5 V reverse bias with an estimated internal quantum efficiency of 2.7 - 4.5%. The dark current is found to vary from a few microamps down to less than a nanoamp after a post-implantation annealing of 350°C. The measured photocurrent dependence on input power shows a linear correspondence over more than three decades, and the frequency response of a 250 µm-length p-i-n device is measured to be ~1.7 GHz for a wavelength of λ = 2.2 µm, thus potentially opening up new communication bands for photonic integrated circuits.

Helium-ion-induced radiation damage in LiNbO3 thin-film electro-optic modulators.

Helium-ion-induced radiation damage in a LiNbO3-thin-film (10 µm-thick) modulator is experimentally investigated. The results demonstrate a degradation of the device performance in the presence of He(+) irradiation at doses of ≥ 10(16) cm(-2). The experiments also show that the presence of the He(+) stopping region, which determines the degree of overlap between the ion-damaged region and the guided optical mode, plays a major role in determining the degree of degradation in modulation performance. Our measurements showed that the higher overlap can lead to an additional ~5.5 dB propagation loss. The irradiation-induced change of crystal-film anisotropy(n(o)-n(e))of ~36% was observed for the highest dose used in the experiments. The relevant device extinction ratio, V(π)L, and device insertion loss, as well the damage mechanisms of each of these parameters are also reported and discussed.