Age estimation by volumetric analysis of teeth using cone beam computed tomography

OBJECTIVES

To evaluate the feasibility of using the pulp volume (Pv) to total volume (Tv) ratio (Pv:Tv), obtained from cone beam computed tomography (CBCT) scans of single-rooted teeth, for age estimation in a Brazilian population sample.

METHODS

After obtaining approval from the ethics committee, the study commenced by applying inclusion criteria to screen CBCT scans, resulting in a probability-based sample of participants aged 18 years and older (ranging from 18 to 82 years, with a mean age of 46.44 years). A total of 517 single-rooted teeth, including maxillary central incisors (CI), mandibular canines (C), and mandibular first premolars (FP), were chosen based on excellent agreement values (> 0.9). Pv and Tv measurements were conducted using semi-automatic segmentation with ITK-SNAP 3.8 software. Statistical analysis was performed using Jamovi software, with a significance level set at 5% (α = 0.05).

RESULTS

A strong negative correlation (r > -0.7) was observed between chronological age and the Pv:Tv ratio across all examined teeth. However, when conducting regression analysis with Pv:Tv data and chronological age as the independent variable, only the mandibular FP teeth exhibited a normal distribution. The resulting linear model demonstrated moderate predictive value (approximately 64%) in explaining the variance in chronological age, but caution should be exercised when interpreting these findings.

CONCLUSIONS

The method of measuring individual tooth volume using CBCT to estimate chronological age via Pv:Tv has been demonstrated as effective and reproducible within the Brazilian population sample.

[Current MR imaging of cartilage in the context of knee osteoarthritis (part 1) : Principles and sequences]

Magnetic resonance imaging (MRI) is the clinical method of choice for cartilage imaging in the context of degenerative and nondegenerative joint diseases. The MRI-based definitions of osteoarthritis rely on the detection of osteophytes, cartilage pathologies, bone marrow edema and meniscal lesions but currently a scientific consensus is lacking. In the clinical routine proton density-weighted, fat-suppressed 2D turbo spin echo sequences with echo times of 30-40 ms are predominantly used, which are sufficiently sensitive and specific for the assessment of cartilage. The additionally acquired T1-weighted sequences are primarily used for evaluating other intra-articular and periarticular structures. Diagnostically relevant artifacts include magic angle and chemical shift artifacts, which can lead to artificial signal enhancement in cartilage or incorrect representations of the subchondral lamina and its thickness. Although scientifically validated, high-resolution 3D gradient echo sequences (for cartilage segmentation) and compositional MR sequences (for quantification of physical tissue parameters) are currently reserved for scientific research questions. The future integration of artificial intelligence techniques in areas such as image reconstruction (to reduce scan times while maintaining image quality), image analysis (for automated identification of cartilage defects), and image postprocessing (for automated segmentation of cartilage in terms of volume and thickness) will significantly improve the diagnostic workflow and advance the field further.

[Digital transformation of a routine histopathology lab : Dos and don'ts!]

The implementation of digital histopathology in the laboratory marks a crucial milestone in the overall digital transformation of pathology. This shift offers a range of new possibilities, including access to extensive datasets for AI-assisted analyses, the flexibility of remote work and home office arrangements for specialists, and the expedited and simplified sharing of images and data for research, conferences, and tumor boards. However, the transition to a fully digital workflow involves significant technological and personnel-related efforts. It necessitates careful and adaptable change management to minimize disruptions, particularly in the personnel domain, and to prevent the loss of valuable potential from employees who may be resistant to change. This article consolidates our institute's experiences, highlighting technical and personnel-related challenges encountered during the transition to digital pathology. It also presents a comprehensive overview of potential difficulties at various interfaces when converting routine operations to a digital workflow.

Three-dimensional aortic geometry mapping via registration of non-gated contrast-enhanced or gated and respiratory-navigated MR angiographies

BACKGROUND

The measurement of aortic dimensions and their evolution are key in the management of patients with aortic diseases. Manual assessment, the current guideline-recommended method and clinical standard, is subjective, poorly reproducible, and time-consuming, limiting the capacity to track aortic growth in everyday practice. Aortic geometry mapping (AGM) via image registration of serial computed tomography angiograms outperforms manual assessment, providing accurate and reproducible 3D maps of aortic diameter and growth rate. This observational study aimed to evaluate the accuracy and reproducibility of AGM on non-gated contrast-enhanced (CE-) and cardiac- and respiratory-gated (GN-) magnetic resonance angiographies (MRA).

METHODS

Patients with thoracic aortic disease followed with serial CE-MRA (n = 30) or GN-MRA (n = 15) acquired at least 1 year apart were retrospectively and consecutively identified. Two independent observers measured aortic diameters and growth rates (GR) manually at several thoracic aorta reference levels and with AGM. Agreement between manual and AGM measurements and their inter-observer reproducibility were compared. Reproducibility for aortic diameter and GR maps assessed with AGM was obtained.

RESULTS

Mean follow-up was 3.8 ± 2.3 years for CE- and 2.7 ± 1.6 years for GN-MRA. AGM was feasible in the 93% of CE-MRA pairs and in the 100% of GN-MRA pairs. Manual and AGM diameters showed excellent agreement and inter-observer reproducibility (ICC>0.9) at all anatomical levels. Agreement between manual and AGM GR was more limited, both in the aortic root by GN-MRA (ICC=0.47) and in the thoracic aorta, where higher accuracy was obtained with GN- than with CE-MRA (ICC=0.55 vs 0.43). The inter-observer reproducibility of GR by AGM was superior compared to manual assessment, both with CE- (thoracic: ICC= 0.91 vs 0.51) and GN-MRA (root: ICC=0.84 vs 0.52; thoracic: ICC=0.93 vs 0.60). AGM-based 3D aortic size and growth maps were highly reproducible (median ICC >0.9 for diameters and >0.80 for GR).

CONCLUSION

Mapping aortic diameter and growth on MRA via 3D image registration is feasible, accurate and outperforms the current manual clinical standard. This technique could broaden the possibilities of clinical and research evaluation of patients with aortic thoracic diseases.

Complimentary Cardiac Computed Tomography Ventricular Volumetry-Derived Metrics of Severity in Patients with Ebstein Anomaly: Comparison with Echocardiography-Based Severity Indices

Detailed three-dimensional cardiac segmentations using cardiac computed tomography (CT) data is technically feasible in patients with Ebstein anomaly, but its complementary role has not been evaluated. This single-center, retrospective study was aimed to evaluate the complementary role of cardiac CT ventricular volumetry in evaluating the severity of Ebstein anomaly. Preoperative cardiac CT ventricular volumetry was performed in 21 children with Ebstein anomaly. CT-based ventricular functional measures were compared between Carpentier types, and between definitive surgical repair types. The Celermajer severity index measured with echocardiography was correlated with CT-based functional parameters. Total right ventricle (RV) and functional RV (fRV) volumes, fRV fraction, fRV/left ventricle (LV) volume ratio, and end-diastolic CT severity index demonstrated statistically significant differences between Carpentier type A/B and Carpentier type C/D (p < 0.05). The Celermajer severity index measured with echocardiography showed a high positive correlation with the end-diastolic CT severity index (R = 0.720, p < 0.002). There were no statistically significant differences in both echocardiography- and CT-based functional measures between patients with biventricular repair and patients with one-and-a-half or univentricular repair (p > 0.05). Compared with echocardiography, cardiac CT ventricular volumetry can provide the severity of Ebstein anomaly objectively and may be used in select patients when echocardiographic results are inconclusive or inconsistent.

Feasibility of Two-Screw Anterior Fixation for Odontoid Fractures in a Chinese Population: A Morphometric Study Based on Computed Tomography

Background

To evaluate the feasibility of treating odontoid fractures in the Chinese population with two cortical screws based on computed tomography (CT) scans and describe a new measurement strategy to guide screw insertion in treating these fractures.

Methods

A retrospective review of cervical computed tomographic scans of 128 patients (aged 18-76 years; men, 55 [43.0%]) was performed. The minimum external transverse diameter (METD), minimum external anteroposterior diameter (MEAD), maximum screw length (MSL), and screw projection back angle (SPBA) of the odontoid process were measured on coronal and sagittal CT images.

Results

The mean values of METD and MEAD were 10.0 ± 1.1 mm and 12.0 ± 1.0 mm, respectively, in men and 9.2 ± 1.0 mm and 11.0 ± 1.0 mm, respectively, in women. Both measurements were significantly higher in men (p < 0.001). In total, 87 individuals (68%) had METD > 9.0 mm that could accommodate two 3.5-mm cortical screws. The mean MSL value and SPBA range were 34.4 ± 2.9 mm and 13.5°-24.2°, respectively, with no statistically significant difference between men and women.

Conclusions

The insertion of two 3.5-mm cortical screws was possible for anterior fixation of odontoid fractures in 87 patients (68%) in our study, and there was a statistically significant difference between men and women.

Partial voxel interpolation to reduce partial volume error of cardiac computed tomography ventricular volumetry in patients with congenital heart disease

BACKGROUND

Varying degrees of partial volume error depending on the complexity of the endocardial borders are inevitable in threshold-based cardiac computed tomography (CT) ventricular volumetry. These errors can potentially be reduced by using a partial voxel interpolation (PVI) method, but this has not been tested for cardiac CT ventricular volumetry.

OBJECTIVE

To evaluate the partial volume error-reducing effects of the PVI method in cardiac CT ventricular volumetry among patients with congenital heart disease (CHD).

MATERIALS AND METHODS

The cardiac CT ventricular volumetry data were obtained from 55 patients (median age 12.0 years) with CHD. The ventricular and myocardial volumes, ejection fraction and ventricular mass-volume ratio were quantified and compared before and after the PVI method. The correlation between the myocardial volumes in the end-systolic and end-diastolic phases was tested. The effect of the PVI method on the classification of ventricular hypertrophy was evaluated.

RESULTS

The indexed ventricular volumes after PVI were significantly smaller (7.4-11.5%) than those before PVI (P<0.001). In contrast, the indexed myocardial volumes were significantly larger (6.2-27.7%) after PVI (P<0.001). The ejection fractions and mass-volume ratios were significantly larger (1.6-2.2% and 19.7-42.5%, respectively) after PVI (P<0.001 and P<0.001, respectively). The indexed myocardial masses showed prominently high correlation between the end-systolic and end-diastolic phases (R, 0.961-0.990; P<0.001). The proportions of no and severe hypertrophy were significantly decreased (P<0.002) and increased (P<0.032), respectively, after the application of the PVI method.

CONCLUSION

The PVI method can reduce partial volume error in cardiac CT ventricular volumetry among patients with CHD.

Statistical shape analysis of corpus callosum in delusional disorder

Neurobiological foundations of delusional disorder (DD) have been studied less with neuroimaging techniques when compared to other psychotic disorders. The present study aimed to delineate the neural substrates of DD by investigating neuroanatomical characteristics of the corpus callosum (CC) with statistical shape analysis (SSA) conducted on magnetic resonance images (MRI). Twenty (female:male=1:1) DSM-5 DD patients and 20 age- and gender-matched healthy individuals were included. High-resolution 3D T1 Turbo Field Echo MRI images were scanned with a 1.5 T MR device. The landmarks that were selected to determine the shape differences in CC were identified based on previous studies. Furthermore, constructed landmarks were determined and employed to better assess regional shape differences. There was no significant difference in the CC area in the mid-sagittal images between the DD patients and controls. However, DD patients exhibited a pattern of structural CC changes in various regions. The study findings emphasizes the variable subregional nature of CC in DD patients. Future SSA studies with larger samples could shed further light on DD etiology, diagnosis, classification and treatment options.

Quantification of airway wall contrast enhancement on virtual monoenergetic images from spectral computed tomography

OBJECTIVES

Quantitative computed tomography (CT) plays an increasingly important role in phenotyping airway diseases. Lung parenchyma and airway inflammation could be quantified by contrast enhancement at CT, but its investigation by multiphasic examinations is limited. We aimed to quantify lung parenchyma and airway wall attenuation in a single contrast-enhanced spectral detector CT acquisition.

METHODS

For this cross-sectional retrospective study, 234 lung-healthy patients who underwent spectral CT in four different contrast phases (non-enhanced, pulmonary arterial, systemic arterial, and venous phase) were recruited. Virtual monoenergetic images were reconstructed from 40-160 keV, on which attenuations of segmented lung parenchyma and airway walls combined for 5th-10th subsegmental generations were assessed in Hounsfield Units (HU) by an in-house software. The spectral attenuation curve slope between 40 and 100 keV (λHU) was calculated.

RESULTS

Mean lung density was higher at 40 keV compared to that at 100 keV in all groups (p < 0.001). λHU of lung attenuation was significantly higher in the systemic (1.7 HU/keV) and pulmonary arterial phase (1.3 HU/keV) compared to that in the venous phase (0.5 HU/keV) and non-enhanced (0.2 HU/keV) spectral CT (p < 0.001). Wall thickness and wall attenuation were higher at 40 keV compared to those at 100 keV for the pulmonary and systemic arterial phase (p ≤ 0.001). λHU for wall attenuation was significantly higher in the pulmonary arterial (1.8 HU/keV) and systemic arterial (2.0 HU/keV) compared to that in the venous (0.7 HU/keV) and non-enhanced (0.3 HU/keV) phase (p ≤ 0.002).

CONCLUSIONS

Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition, and may separate arterial and venous enhancement. Further studies are warranted to analyze spectral CT for inflammatory airway diseases.

KEY POINTS

• Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition. • Spectral CT may separate arterial and venous enhancement of lung parenchyma and airway wall. • The contrast enhancement can be quantified by calculating the spectral attenuation curve slope from virtual monoenergetic images.

Supramaximal Resection for Glioblastoma: Redefining the Extent of Resection Criteria and Its Impact on Survival

Glioblastomas (GBMs) are the most common and aggressive primary brain tumors, and despite advances in treatment, prognosis remains poor. The extent of resection has been widely recognized as a key factor affecting survival outcomes in GBM patients. The surgical principle of "maximal safe resection" has been widely applied to balance tumor removal and neurological function preservation. Historically, T1-contrast enhanced (T1CE) extent of resection has been the focus of research; however, the "supramaximal resection" concept has emerged, advocating for even greater tumor resection while maintaining neurological function. Recent studies have demonstrated potential survival benefits associated with resection beyond T1CE extent in GBMs. This review explores the developing consensus and newly established criteria for "supramaximal resection" in GBMs, with a focus on T2-extent of resection. Systematic reviews and meta-analyses on supramaximal resection are summarized, and the Response Assessment in Neuro-Oncology (RANO) resect group classification for extent of resection is introduced. The evolving understanding of the role of supramaximal resection in GBMs may lead to improved patient outcomes and more objective criteria for evaluating the extent of tumor resection.

NEMA NU 1-2018 performance characterization and Monte Carlo model validation of the Cubresa Spark SiPM-based preclinical SPECT scanner

BACKGROUND

The Cubresa Spark is a novel benchtop silicon-photomultiplier (SiPM)-based preclinical SPECT system. SiPMs in SPECT significantly improve resolution and reduce detector size compared to preclinical cameras with photomultiplier tubes requiring highly magnifying collimators. The NEMA NU 1 Standard for Performance Measurements of Gamma Cameras provides methods that can be readily applied or extended to characterize preclinical cameras with minor modifications. The primary objective of this study is to characterize the Spark according to the NEMA NU 1-2018 standard to gain insight into its nuclear medicine imaging capabilities. The secondary objective is to validate a GATE Monte Carlo simulation model of the Spark for use in preclinical SPECT studies.

METHODS

NEMA NU 1-2018 guidelines were applied to characterize the Spark's intrinsic, system, and tomographic performance with single- and multi-pinhole collimators. Phantoms were fabricated according to NEMA specifications with deviations involving high-resolution modifications. GATE was utilized to model the detector head with the single-pinhole collimator, and NEMA measurements were employed to tune and validate the model. Single-pinhole and multi-pinhole SPECT data were reconstructed with the Software for Tomographic Image Reconstruction and HiSPECT, respectively.

RESULTS

The limiting intrinsic resolution was measured as 0.85 mm owing to a high-resolution SiPM array combined with a 3 mm-thick scintillation crystal. The average limiting tomographic resolution was 1.37 mm and 1.19 mm for the single- and multi-pinhole collimators, respectively, which have magnification factors near unity at the center of rotation. The maximum observed count rate was 15,400 cps, and planar sensitivities of 34 cps/MBq and 150 cps/MBq were measured at the center of rotation for the single- and multi-pinhole collimators, respectively. All simulated tests agreed well with measurement, where the most considerable deviations were below 7%.

CONCLUSIONS

NEMA NU 1-2018 standards determined that a SiPM detector mitigates the need for highly magnifying pinhole collimators while preserving detailed information in projection images. Measured and simulated NEMA results were highly comparable with differences on the order of a few percent, confirming simulation accuracy and validating the GATE model. Of the collimators initially provided with the Spark, the multi-pinhole collimator offers high resolution and sensitivity for organ-specific imaging of small animals, and the single-pinhole collimator enables high-resolution whole-body imaging of small animals.

Identification of rapid progression of right ventricular functional measures using three-dimensional cardiac computed tomography after total surgical correction of tetralogy of Fallot

OBJECTIVE

To identify subsets of patients with tetralogy of Fallot (TOF) after total surgical correction demonstrating the rapid progression of right ventricle (RV) functional measures using cardiac computed tomography (CT) ventricular volumetry.

METHODS

Rapid or slow progression of RV functional measures was determined in 109 patients with TOF who underwent cardiac CT ventricular volumetry more than twice after total surgical correction. Patient age, body surface area, postoperative days, the time interval between the first and last cardiac CT examinations, and CT-based functional measures were evaluated using binary logistic regression to determine the predictors of the rapid progression. Receiver operating characteristic curve analysis was performed to evaluate diagnostic performance of the potential predictors.

RESULTS

The rapid progression of indexed RV end-systolic volume (ESV) (≥2.7 mL/m²/year) and indexed RV end-diastolic volume (≥0.9 mL/m²/year) could be predicted by RV ejection fraction (EF) at the last cardiac CT with an odds ratio of 1.340 (95 % confidence interval [CI], 1.122-1.600; p = 0.001) and age at the last cardiac CT with an odds ratio of 8.255 (95 % CI, 1.531-44.513; p = 0.014), respectively. RV EF at the last cardiac CT showed the highest diagnostic performance (area under the curve = 0.799; p < 0.002) for the rapid progression of indexed RV ESV.

CONCLUSION

Cardiac CT ventricular volumetry can be used to identify patients demonstrating the rapid progression of RV functional measures after total surgical correction of TOF and follow-up imaging protocols can be individually optimized based on initial progression rate.

[Preliminary evaluation of the trueness of 5 chairside 3D facial scanning techniques]

OBJECTIVE

To quantitatively evaluate the trueness of five chairside three-dimensional facial scanning techniques, and to provide reference for the application of oral clinical diagnosis and treatment.

METHODS

The three-dimensional facial data of the subjects were collected by the traditional professional three-dimensional facial scanner Face Scan, which was used as the reference data of this study. Four kinds of portable three-dimensional facial scanners (including Space Spider, LEO, EVA and DS-FScan) and iPhone Ⅹ mobile phone (Bellus3D facial scanning APP) were used to collect three-dimensional facial data from the subjects. In Geomagic Studio 2013 software, through data registration, deviation analysis and other functions, the overall three-dimensional deviation and facial partition three-dimensional deviation of the above five chairside three-dimensional facial scanning technologies were calculated, and their trueness performance evaluated. Scanning time was recorded during the scanning process, and the subject's comfort was scored by visual analogue scale(VAS). The scanning efficiency and patient acceptance of the five three-dimensional facial scanning techniques were evaluated.

RESULTS

DS-FScan had the smallest mean overall and mean partition three-dimensional deviation between the test data and the reference data, which were 0.334 mm and 0.329 mm, respectively. The iPhone Ⅹ mobile phone had the largest mean overall and mean partition three-dimensional deviation between the test data and the reference data, which were 0.483 mm and 0.497 mm, respectively. The detailed features of the three-dimensional facial data obtained by Space Spider were the best. The iPhone Ⅹ mobile phone had the highest scanning efficiency and the highest acceptance by the subject. The average scanning time of the iPhone Ⅹ mobile phone was 14 s, and the VAS score of the subjects' scanning comfort was 9 points.

CONCLUSION

Among the five chairside three-dimensional face scanning technologies, the trueness of the scan data of the four portable devices had no significant difference, and they were all better than the iPhone Ⅹ mobile phone scan. The subject with the iPhone Ⅹ scanning technology had the best expe-rience.

Automated brain volumetric program measuring regional brain atrophy in diagnosis of mild cognitive impairment and Alzheimer's disease dementia

A quantitative analysis of brain volume can assist in the diagnosis of Alzheimer's disease (AD) which is ususally accompanied by brain atrophy. With an automated analysis program Quick Brain Volumetry (QBraVo) developed for volumetric measurements, we measured regional volumes and ratios to evaluate their performance in discriminating AD dementia (ADD) and mild cognitive impairment (MCI) patients from normal controls (NC). Validation of QBraVo was based on intra-rater and inter-rater reliability with a manual measurement. The regional volumes and ratios to total intracranial volume (TIV) and to total brain volume (TBV) or total cerebrospinal fluid volume (TCV) were compared among subjects. The regional volume to total cerebellar volume ratio named Standardized Atrophy Volume Ratio (SAVR) was calculated to compare brain atrophy. Diagnostic performances to distinguish among NC, MCI, and ADD were compared between MMSE, SAVR, and the predictive model. In total, 56 NCs, 44 MCI, and 45 ADD patients were enrolled. The average run time of QBraVo was 5 min 36 seconds. Intra-rater reliability was 0.999. Inter-rater reliability was high for TBV, TCV, and TIV (R = 0.97, 0.89 and 0.93, respectively). The medial temporal SAVR showed the highest performance for discriminating ADD from NC (AUC = 0.808, diagnostic accuracy = 80.2%). The predictive model using both MMSE and medial temporal SAVR improved the diagnostic performance for MCI in NC (AUC = 0.844, diagnostic accuracy = 79%). Our results demonstrated QBraVo is a fast and accurate method to measure brain volume. The regional volume calculated as SAVR could help to diagnose ADD and MCI and increase diagnostic accuracy for MCI.

Prediction of pulmonary hypertension using central-to-peripheral pulmonary vascular volume ratio on three-dimensional cardiothoracic CT in patients with congenital heart disease

PURPOSE

To evaluate whether the central-to-peripheral pulmonary vascular volume ratio measured using three-dimensional cardiothoracic CT can serve as a potential predictor of pulmonary hypertension (PH) in patients with congenital heart disease.

METHODS

Cardiothoracic CT was used to quantify right and left total, central, and peripheral pulmonary vascular volumes segmented based on a three-dimensional threshold-based approach in 60 patients with congenital heart disease (group with PH, n = 30; group without PH, n = 30). The CT-based central-to-peripheral pulmonary vascular volume ratios were correlated with the echocardiography-based maximum velocity of tricuspid regurgitation (TR Vmax) and systolic pulmonary arterial pressure (PAP) values. The diagnostic ability of the central-to-peripheral pulmonary vascular volume ratio to predict PH was analyzed.

RESULTS

The central-to-peripheral pulmonary vascular volume ratios were significantly higher in the group with PH compared to the group without PH (1.6 ± 0.9 vs. 0.8 ± 0.3 for the right side, p < 0.001; 2.4 ± 2.2 vs. 1.0 ± 1.4 for the left side, p < 0.004). The right central-to-peripheral pulmonary volume ratios were significantly positively correlated with the TR Vmax and estimated systolic PAP values (R = 0.627 and 0.633, respectively; p < 0.001) in patients with and without PH, while the ratios were moderately correlated with the TR Vmax and estimated systolic PAP values (R = 0.431 and 0.435, respectively; p < 0.020) in the group with PH. The right and left central-to-peripheral pulmonary vascular volume ratios demonstrated a good diagnostic ability for predicting the presence of PH (area under the receiver-operating characteristic curve = 0.867, p < 0.001 and 0.859, p < 0.001 for the right and left, respectively).

CONCLUSION

The CT-based central-to-peripheral pulmonary vascular volume ratio can be used to predict PH in patients with congenital heart disease.

Registration-based semi-automatic assessment of aortic diameter growth rate from contrast-enhanced computed tomography outperforms manual quantification

OBJECTIVES

Manual assessment of aortic diameters on double-oblique reformatted computed tomography angiograms (CTA) is considered the current standard, although the reproducibility for growth rates has not been reported. Deformable registration of CTA has been proposed to provide 3D aortic diameters and growth maps, but validation is lacking. This study aimed to quantify accuracy and inter-observer reproducibility of registration-based and manual assessment of aortic diameters and growth rates.

METHODS

Forty patients with ≥ 2 CTA acquired at least 6 months apart were included. Aortic diameters and growth rate were obtained in the aortic root and the entire thoracic aorta using deformable image registration by two independent observers, and compared with the current standard at typical anatomical landmarks.

RESULTS

Compared with manual assessment, the registration-based technique presented low bias (0.46 mm), excellent agreement (ICC = 0.99), and similar inter-observer reproducibility (ICC = 0.99 for both) for aortic diameters; and low bias (0.10 mm/year), good agreement (ICC = 0.82), and much higher inter-observer reproducibility for growth rates (root: ICC = 0.96 vs 0.68; thoracic aorta: ICC = 0.96 vs 0.80). Registration-based growth rate reproducibility over a 6-month-long follow-up was similar to that obtained by manual assessment after 2.7 years (LoA = [- 0.01, 0.33] vs [- 0.13, 0.21] mm/year, respectively). Mapping of diameter and growth rate was highly reproducible (ICC > 0.9) in the whole thoracic aorta.

CONCLUSIONS

Registration-based assessment of aortic dilation on CTA is accurate and substantially more reproducible than the current standard, even at follow-up as short as 6 months, and provides robust 3D mapping of aortic diameters and growth rates beyond the pre-established anatomic landmarks.

KEY POINTS

• Registration-based semi-automatic assessment of progressive aortic dilation on CTA is accurate and substantially more reproducible than the current standard. • The registration-based technique allows robust growth rate assessment at follow-up as short as 6 months, with a similar reproducibility to that obtained by manual assessment at around 3 years. • The use of image registration provides robust 3D mapping of aortic diameters and growth rates beyond the pre-established anatomic landmarks.

Validity and reliability of masseter muscles segmentation from the transverse sections of Cone-Beam CT scans compared with MRI scans

BACKGROUND

To evaluate the validity and reliability of cone-beam computed tomography (CBCT) masseter muscle segmentation by comparing with the magnetic resonance imaging (MRI) masseter muscle segmentation of the same patients.

METHODS

Seventeen volunteers were included in this study. CBCT and MRI scans of the volunteers were taken, respectively, within one month. The masseter muscles in the CBCT scans were segmented by a generative adversarial network (GAN)-based framework combined with manual check. The masseter muscles in the MRI scans were segmented manually. The segmentations were repeated by the first examiner and a second examiner. For cross-sectional area (CSA), paired t-test, intraclass correlation coefficient (ICC) and standard error of measurement (SEM) were calculated to evaluate the validity and reliability of the segmentations. The validity and reliability were also calculated by Dice similarity coefficient (DSC) and average Hausdorff distance (aHD) between different segmentations. Seventeen volunteers were included in this study. CBCT and MRI scans of the volunteers were taken, respectively, within one month. The masseter muscles in the CBCT scans were segmented by a generative adversarial network (GAN)-based framework combined with manual check. The masseter muscles in the MRI scans were segmented manually. The segmentations were repeated by the first examiner and a second examiner. For cross-sectional area (CSA), paired t-test, intraclass correlation coefficient (ICC) and standard error of measurement (SEM) were calculated to evaluate the validity and reliability of the segmentations. The validity and reliability were also calculated by Dice similarity coefficient (DSC) and average Hausdorff distance (aHD) between different segmentations.

RESULTS

Paired t-test showed that there was no significant difference in CSA between CBCT and MRI masseter segmentations. The ICCs were all larger than 0.95 and the SEM was less than 4.85 mm² for CSA. The DSC was all larger than 0.95 showing over 95% of similarity between CBCT and MRI masseter segmentations. The aHD was all smaller than 0.09 mm showing great consistency of the contour of CBCT and MRI segmentations.

CONCLUSION

Masseter muscle segmentation from CBCT scans was not significantly different from the segmentation from MRI scans. CBCT muscle segmentation showed great validity compared with MRI scans, and great reliability in retests.

The precision of gingival recession measurements is increased by an automated curvature analysis method

Background

The extent of gingival recession represents one of the most important measures determining outcome of periodontal plastic surgery. The accurate measurements are, thus, critical for optimal treatment planning and outcome evaluation. Present study aimed to introduce automated curvature-based digital gingival recession measurements, evaluate the agreement and reliability of manual measurements, and identify sources of manual variability.

Methods

Measurement of gingival recessions was performed manually by three examiners and automatically using curvature analysis on representative cross-sections (n = 60). Cemento-enamel junction (CEJ) and gingival margin (GM) measurement points selection was the only variable. Agreement and reliability of measurements were analysed using intra- and inter-examiner correlations and Bland–Altman plots. Measurement point selection variability was evaluated with manual point distance deviation from an automatic point. The effect of curvature on manual point selection was evaluated with scatter plots.

Results

Bland–Altman plots revealed a high variability of examiner’s recession measurements indicated by high 95% limits of agreement range of approximately 1 mm and several outliers beyond the limits of agreement. CEJ point selection was the main source of examiner’s variability due to smaller curvature values than GM, i.e., median values of − 0.98 mm^− 1 and − 4.39 mm^− 1, respectively, indicating straighter profile for CEJ point. Scatter plots revealed inverse relationship between curvature and examiner deviation for CEJ point, indicating a threshold curvature value around 1 mm^− 1.

Conclusions

Automated curvature-based approach increases the precision of recession measurements by reproducible measurement point selection. Proposed approach allows evaluation of teeth with indistinguishable CEJ that could be not be included in the previous studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-021-01858-9.

Liver segmental volume and attenuation ratio (LSVAR) on portal venous CT scans improves the detection of clinically significant liver fibrosis compared to liver segmental volume ratio (LSVR)

Background

The aim of this proof-of-concept study was to show that the liver segmental volume and attenuation ratio (LSVAR) improves the detection of significant liver fibrosis on portal venous CT scans by adding the liver vein to cava attenuation (LVCA) to the liver segmental volume ratio (LSVR).

Material and methods

Patients who underwent portal venous phase abdominal CT scans and MR elastography (reference standard) within 3 months between 02/2016 and 05/2017 were included. The LSVAR was calculated on portal venous CT scans as LSVR*LVCA, while the LSVR represented the volume ratio between Couinaud segments I-III and IV-VIII, and the LVCA represented the density of the liver veins compared to the density in the vena cava. The LSVAR and LSVR were compared between patients with and without significantly elevated liver stiffness (based on a cutoff value of 3.5 kPa) using the Mann–Whitney U test and ROC curve analysis.

Results

The LSVR and LSVAR allowed significant differentiation between patients with (n = 19) and without (n = 122) significantly elevated liver stiffness (p < 0.001). However, the LSVAR showed a higher area under the curve (AUC = 0.96) than the LSVR (AUC = 0.74). The optimal cutoff value was 0.34 for the LSVR, which detected clinically increased liver stiffness with a sensitivity of 53% and a specificity of 88%. With a cutoff value of 0.67 for the LSVAR, the sensitivity increased to 95% while maintaining a specificity of 89%.

Conclusion

The LSVAR improves the detection of significant liver fibrosis on portal venous CT scans compared to the LSVR.

Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction

OBJECTIVE

To evaluate the effect of a commercial deep learning algorithm on the image quality of chest CT, focusing on the upper abdomen.

METHODS

One hundred consecutive patients who simultaneously underwent contrast-enhanced chest and abdominal CT were collected. The radiation dose was optimized for each scan (mean CTDI_vol: chest CT, 3.19 ± 1.53 mGy; abdominal CT, 7.10 ± 1.88 mGy). Three image sets were collected: chest CT reconstructed with an adaptive statistical iterative reconstruction (ASiR-CHT; 50% blending), chest CT with a deep learning algorithm (DLIR-CHT), and abdominal CT with ASiR (ASiR-ABD; 40% blending). Afterwards, the images covering the upper abdomen were extracted, and image noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured. For subjective evaluation, three radiologists independently assessed noise, spatial resolution, presence of artifacts, and overall image quality. Additionally, readers selected the most preferable reconstruction technique among three image sets for each case.

RESULTS

The average measured noise for DLIR-CHT, ASiR-CHT, and ASiR-ABD was 8.01 ± 2.81, 14.8 ± 2.56, and 12.3 ± 2.28, respectively (p < .001). Deep learning-based image reconstruction (DLIR) also showed the best SNR and CNR (p < .001). However, in the subjective analysis, ASiR-ABD showed less subjective noise than DLIR (2.94 ± 0.23 vs. 2.87 ± 0.26; p < .001), while DLIR showed better spatial resolution (2.60 ± 0.34 vs. 2.44 ± 0.31; p = .02). ASiR-ABD showed a better overall image quality (p = .001), but two of the three readers preferred DLIR more frequently.

CONCLUSION

With < 50% of the radiation dose, DLIR chest CT showed comparable image quality in the upper abdomen to that of dedicated abdominal CT and was preferred by most readers.

KEY POINTS

• With < 50% radiation dose, a deep learning algorithm applied to contrast-enhanced chest CT exhibited better image noise and signal-to-noise ratio than standard abdominal CT with the ASiR technique. • Pooled readers mostly preferred deep learning algorithm-reconstructed contrast-enhanced chest CT reconstructed using a standard ASiR-reconstructed abdominal CT. • Reconstruction algorithm-induced distortion artifacts were more frequently observed on deep learning algorithm-reconstructed images, but diagnostic difficulty was reported in only 0.3% of cases.

Automatic segmentation of pulmonary lobes on low-dose computed tomography using deep learning

Background

To develop and validate a fully automated deep learning-based segmentation algorithm to segment pulmonary lobe on low-dose computed tomography (LDCT) images.

Methods

This study presents an automatic segmentation of pulmonary lobes using a fully convolutional neural network named dense V-network (DenseVNet) on lung cancer screening LDCT images. A total of 160 LDCT cases for lung cancer screening (100 in the training set, 10 in the validation set, and 50 in the test set) was included in this study. Specifically, the template of pulmonary lobes (the right lung consists of three lobes, and the left lung consists of two lobes) were obtained from pixel-level annotations by semiautomatic segmentation platform. Then, the model was trained under the supervision of the LDCT training set. Finally, the trained model was used to segment the LDCT in the test set. Dice coefficient, Jaccard coefficient, and Hausdorff distance were adopted as evaluation metrics to verify the performance of our segmentation model.

Results

In this study, the model achieved the accurate segmentation of each pulmonary lobe in seconds without the intervention of researchers. The testing set consisted 50 LDCT cases were used to evaluate the performance of the segmentation model. The all-lobes Dice coefficient of the test set was 0.944, the Jaccard coefficient was 0.896, and the Hausdorff distance was 92.908 mm.

Conclusions

The segmentation model based on LDCT can automatically and robustly and efficiently segment pulmonary lobes. It will provide effective location information and contour constraints for pulmonary nodule detection on LDCT images for lung cancer screening, which may have potential clinical application.

Automatic quantification of myocardium and pericardial fat from coronary computed tomography angiography: a multicenter study

OBJECTIVES

To develop a deep learning-based method for simultaneous myocardium and pericardial fat quantification from coronary computed tomography angiography (CCTA) for the diagnosis and treatment of cardiovascular disease (CVD).

METHODS

We retrospectively identified CCTA data obtained between May 2008 and July 2018 in a multicenter (six centers) CVD study. The proposed method was evaluated on 422 patients' data by two studies. The first overall study involves training model on CVD patients and testing on non-CVD patients, as well as training on non-CVD patients and testing on CVD patients. The second study was performed using the leave-center-out approach. The method performance was evaluated using Dice similarity coefficient (DSC), Jaccard index (JAC), 95% Hausdorff distance (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and the center of mass distance (CMD). The robustness of the proposed method was tested using the nonparametric Kruskal-Wallis test and post hoc test to assess the equality of distribution of DSC values among different tests.

RESULTS

The automatic segmentation achieved a strong correlation with contour (ICC and R > 0.97, p value < 0.001 throughout all tests). The accuracy of the proposed method remained high through all the tests, with the median DSC higher than 0.88 for pericardial fat and 0.96 for myocardium. The proposed method also resulted in mean MSD, RMSD, HD95, and CMD of less than 1.36 mm for pericardial fat and 1.00 mm for myocardium.

CONCLUSIONS

The proposed deep learning-based segmentation method enables accurate simultaneous quantification of myocardium and pericardial fat in a multicenter study.

KEY POINTS

• Deep learning-based myocardium and pericardial fat segmentation method tested on 422 patients' coronary computed tomography angiography in a multicenter study. • The proposed method provides segmentations with high volumetric accuracy (ICC and R > 0.97, p value < 0.001) and similar shape as manual annotation by experienced radiologists (median Dice similarity coefficient ≥ 0.88 for pericardial fat and 0.96 for myocardium).

[Artificial intelligence and machine learning in oncologic imaging]

Machine learning (ML) is entering many areas of society, including medicine. This transformation has the potential to drastically change medicine and medical practice. These aspects become particularly clear when considering the different stages of oncologic patient care and the involved interdisciplinary and intermodality interactions. In recent publications, computers-in collaboration with humans or alone-have been outperforming humans regarding tumor identification, tumor classification, estimating prognoses, and evaluation of treatments. In addition, ML algorithms, e.g., artificial neural networks (ANNs), which constitute the drivers behind many of the latest achievements in ML, can deliver this level of performance in a reproducible, fast, and inexpensive manner. In the future, artificial intelligence applications will become an integral part of the medical profession and offer advantages for oncologic diagnostics and treatment.

Validation of a Web-Based Planning Tool for Percutaneous Cryoablation of Renal Tumors

Purpose

To validate a simulation environment for virtual planning of percutaneous cryoablation of renal tumors.

Materials and Methods

Prospectively collected data from 19 MR-guided procedures were used for validation of the simulation model. Volumetric overlap of the simulated ablation zone volume (Σ) and the segmented ablation zone volume (S; assessed on 1-month follow-up scan) was quantified. Validation metrics were DICE Similarity Coefficient (DSC; the ratio between twice the overlapping volume of both ablation zones divided by the sum of both ablation zone volumes), target overlap (the ratio between the overlapping volume of both ablation zones to the volume of S; low ratio means S is underestimated), and positive predictive value (the ratio between the overlapping volume of both ablation zones to the volume of Σ; low ratio means S is overestimated). Values were between 0 (no alignment) and 1 (perfect alignment), a value > 0.7 is considered good.

Results

Mean volumes of S and Σ were 14.8 cm³ (± 9.9) and 26.7 cm³ (± 15.0), respectively. Mean DSC value was 0.63 (± 0.2), and ≥ 0.7 in 9 cases (47%). Mean target overlap and positive predictive value were 0.88 (± 0.11) and 0.53 (± 0.24), respectively. In 17 cases (89%), target overlap was ≥ 0.7; positive predictive value was ≥ 0.7 in 4 cases (21%) and < 0.6 in 13 cases (68%). This indicates S is overestimated in the majority of cases.

Conclusion

The validation results showed a tendency of the simulation model to overestimate the ablation effect. Model adjustments are necessary to make it suitable for clinical use.

Thickness change of masseter muscles and the surrounding soft tissues in female patients during orthodontic treatment: a retrospective study

BACKGROUND

Facial esthetics is a major concern of orthodontic patients. This study aims to evaluate orthodontic treatment-related thickness changes of the masseter muscles and surrounding soft tissues and the potential factors that would influence these changes during orthodontic treatment in female adults.

METHODS

Forty-two female adult patients were included in this retrospective study and were divided into extraction (n = 22) and nonextraction (n = 20) groups. Pretreatment and posttreatment cone-beam computed tomography (CBCT) images were superimposed and reconstructed. The thickness changes of the masseter area of facial soft tissue (MAS), masseter muscles (MM) and surrounding fat tissue (FT) were measured. Pretreatment age, treatment duration, sagittal relationship (ANB), and vertical relationship (Frankfort-mandibular plane angle, FMA)-related MAS, MM and FT changes were compared between extraction and nonextraction groups. Spearman's correlation coefficient was calculated between the above variables. Regression analysis was conducted to confirm the causal relations of the variables.

RESULTS

The thickness of MAS and MM significantly decreased in both groups, with larger decreases (> 1 mm) in the extraction group. There were strong correlations (r > 0.7) between the thickness decrease in MAS and MM in both groups and moderate correlations (r > 0.4) between MAS and FT in the nonextraction group. A significantly greater decrease of MAS and MM were found to be moderately correlated with a smaller FMA (r > 0.4) in the extraction group. Scatter plots and regression analysis confirmed these correlations.

CONCLUSIONS

Masseter muscles and the surrounding soft tissue exhibited a significant decrease in thickness during orthodontic treatment in female adults. Low-angle patients experienced a greater decrease in soft tissue thickness in the masseter area in the extraction case. But the thickness changes were clinically very small in most patients.

Effects of five levels of noise reduction applied to indirect digital radiography on diagnostic accuracy of external apical root resorption

BACKGROUND

Radiologic diagnosis of external apical root resorption (EARR) is clinically important. Noise might disrupt this diagnosis. Therefore, we assessed the efficacy of noise reduction on periapical indirect digital radiography.

MATERIALS AND METHODS

This in vitro study as performed on 792 radiographs. A total of 66 single-rooted premolars were inserted in dried hemimandibles of sheep and fixed with modeling wax. Digital images were obtained using the parallel technique. The storage phosphor plates were processed in the DIGORA Optime scanner. The resulting images were sent to a computer using the Scanora software for radiographic analysis. The teeth were removed from the mandible, and artificial EARR defects were simulated. Afterward, the indirect digital radiographs were obtained at the same condition of the baseline. Five levels of noise reduction were applied. All images were saved in Digital Imaging and Communications in Medicine format and monitored by two observers twice over 2 weeks. Data were analyzed statistically using Cochran and McNemar tests (α = 0.05).

RESULTS

The highest sensitivity rate was found in the baseline group (0.99), and the lowest sensitivity was related to the "four-time noise reduction" method (0.91). The highest specificity rate was in the "five-times noise reduction" method (0.88) and the lowest specificity was associated with "one-time noise reduction" method (0.71). There was no statistical difference between images with/without noise reduction enhancement with varied gradation levels in terms of diagnostic accuracies of EARR (P > 0.05).

CONCLUSION

Application of noise reduction procedure in Scanora software might have no effect on the accuracy of EARR diagnosis.

Resectable pancreatic ductal adenocarcinoma: association between preoperative CT texture features and metastatic nodal involvement

Background

To explore the relationship between the lymph node status and preoperative computed tomography images texture features in pancreatic cancer.

Methods

A total of 155 operable pancreatic cancer patients (104 men, 51 women; mean age 63.8 ± 9.6 years), who had undergone contrast-enhanced computed tomography in the arterial and portal venous phases, were enrolled in this retrospective study. There were 73 patients with lymph node metastases and 82 patients without nodal involvement. Four different data sets, with thin (1.25 mm) and thick (5 mm) slices (at arterial phase and portal venous phase) were analysed. Texture analysis was performed by using MaZda software. A combination of feature selection algorithms was used to determine 30 texture features with the optimal discriminative performance for differentiation between lymph node positive and negative groups. The prediction performance of the selected feature was evaluated by receiver operating characteristic (ROC) curve analysis.

Results

There were 10 texture features with significant differences between two groups and significance in ROC analysis were identified. They were WavEnLH_s-2(wavelet energy with rows and columns are filtered with low pass and high pass frequency bands with scale factors 2) from wavelet-based features, 135dr_LngREmph (long run emphasis in 135 direction) and 135dr_Fraction (fraction of image in runs in 135 direction) from run length matrix-based features, and seven variables of sum average from coocurrence matrix-based features (SumAverg). The ideal cutoff value for predicting lymph node metastases was 270 for WavEnLH_s-2 (positive likelihood ratio 2.08). In addition, 135dr_LngREmph and 135dr_Fraction were correlated with the ratio of metastatic to examined lymph nodes.

Conclusions

Preoperative computed tomography high order texture features provide a useful imaging signature for the prediction of nodal involvement in pancreatic cancer.

High-Throughput Multiplex Immunohistochemical Imaging of the Tumor and Its Microenvironment

PURPOSE

The aim of this study was to develop a formalin-fixed paraffin-embedded (FFPE) tissue based multiplex immunochemistry (mIHC) method for high-throughput comprehensive tissue imaging and demonstrate its feasibility, validity, and usefulness.

MATERIALS AND METHODS

The mIHC protocol was developed and tested on tissue microarray slides made from archived gastric cancer (GC) tissue samples. On a single FFPE slide, cyclic immunochemistry for multiple markers of immune cells and cytokeratin for tumor cells was performed; hematoxylin staining was used for demarcation of nuclei. Whole slides were digitally scanned after each cycle. For interpretation of mIHC results, we performed computer-assisted image analysis using publicly available software.

RESULTS

Using mIHC, we were able to characterize the tumor microenvironment (TME) of GCs with accurate visualization of various immune cells harboring complex immunophenotypes. Spatial information regarding intratumoral and peritumoral TME could be demonstrated by digital segmentation of image guided by cytokeratin staining results. We further extended the application of mIHC by showing that subcellular localization of molecules can be achieved by image analysis of mIHC results.

CONCLUSION

We developed a robust method for high-throughput multiplex imaging of FFPE tissue slides. The feasibility and adaptability of mIHC suggest that it is an efficient method for in situ single-cell characterization and analysis.

Accuracy of computer-assisted image analysis in the diagnosis of maxillofacial radiolucent lesions: A systematic review and meta-analysis

OBJECTIVES

This study aimed to search for scientific evidence concerning the accuracy of computer-assisted analysis for diagnosing maxillofacial radiolucent lesions.

METHODS

A systematic review was conducted according to the statements of Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols and considering 10 databases, including the gray literature. Protocol was registered at the International Prospective Register of Systematic Reviews (CRD42018089945). The population, intervention, comparison and outcome strategy was used to define the eligibility criteria and only diagnostic test studies were included. Their risk of bias was assessed by the Joanna Briggs Institute Critical Appraisal tool. Random-effects model meta-analysis was performed and heterogeneity among the included studies was estimated using the I² statistic. The grade of recommendation, assessment, development, and evaluation (GRADE) tool assessed the quality of evidence and strength of recommendation across included studies.

RESULTS

Out of 715 identified citations, four papers, published between 2009 and 2017, fulfilled the criteria and were included in this systematic review. A total of 191 lesions, classified as periapical granuloma and cyst, dentigerous cyst or keratocystic odontogenic tumor, were analyzed. All selected articles scored low risk of bias. The pooled accuracy estimation, regardless of the classification method used, was 88.75% (95% CI = 85.19-92.30). Heterogeneity test reached moderate values (I² = 57.89%). According to the GRADE tool, the analyzed outcome was classified as having low level of certainty.

CONCLUSIONS

The overall evaluation showed all studies presented high accuracy rates of computer-aided diagnosis systems in classifying radiolucent maxillofacial lesions compared to histopathological biopsy. However, due to the moderate heterogeneity found among the studies included in this meta-analysis, a pragmatic recommendation about the use of computer-assisted analysis is not possible.

Quantification of intranodal vascularity by computer pixel-counting method enhances the accuracy of ultrasound in distinguishing metastatic and tuberculous cervical lymph nodes

Background

Ultrasound is a common imaging method for assessment of cervical lymph nodes. However, metastatic and tuberculous lymph nodes have similar sonographic features in routine ultrasound examination. Computer-aided assessment could be a potential adjunct to enhance the accuracy of differential diagnosis.

Methods

Gray-scale and power Doppler sonograms of 100 patients with palpable cervical lymph nodes were reviewed and analyzed (60 metastatic nodes, 40 tuberculous nodes). Final diagnosis of lymph nodes was based on fine needle aspiration and cytology. Sonograms were reviewed and assessed for nodal shape, echogenic hilus, intranodal necrosis and vascular distribution (conventional assessment). Intranodal vascularity was quantified using a customized computer algorithm to determine vascularity index (VI). The diagnostic accuracy of using conventional assessment and its combination with intranodal VI method was evaluated and compared.

Results

Metastatic and tuberculous nodes tended to be round (75.0% vs. 50.0%), without echogenic hilus (86.7% vs. 72.5%) and have peripheral vascularity (73.3% vs. 85.0%). Intranodal necrosis is more common in tuberculous nodes (27.5%) than metastatic nodes (8.3%). Using conventional assessment in differentiating metastatic and tuberculous nodes, the diagnostic accuracy was 56% with a sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 56.7%, 55%, 65.4% and 45.8% respectively. The VI of metastatic nodes (23.4%±2.1%) was significantly higher than that of tuberculous nodes (12.0%±1.6%) (P<0.05). The optimum cut-off of VI for the differential diagnosis was 20%. By combining conventional assessment and intranodal VI quantification, the diagnostic accuracy was increased to 69% with a sensitivity, specificity, PPV and NPV of 80%, 52.5%, 71.6%, 63.6% respectively. The increase in sensitivity was statistically significant (P=0.006).

Conclusions

Computer-aided quantification of intranodal vascularity provides added value in routine ultrasound assessment of cervical lymph nodes. It enhances the accuracy of ultrasound in distinguishing metastatic and tuberculous cervical lymph nodes.

Metallic dental artifact reduction in computed tomography (Smart MAR): Improvement of image quality and diagnostic confidence in patients with suspected head and neck pathology and oral implants

PURPOSE

We determined whether the Smart MAR metal artifact reduction tool - a three-stage, projection-based, post processing algorithm - improves subjective and objective image quality and diagnostic confidence in patients with dental artifacts and suspected head and neck pathology compared to standard adaptive statistical iterative reconstructions (ASIR V) alone.

METHOD

The study included 100 consecutive patients with nonremovable oral implants or dental fillings and suspected oropharyngeal cancer or abscess. CT raw data of a single-source multislice CT scanner were postprocessed using ASIR V alone and with additional Smart MAR reconstruction. Image quality of baseline ASIR V and Smart MAR-based reconstruction series was compared both quantitatively (5 regions of interest, ROIs) and qualitatively (two independent raters).

RESULTS

Additional Smart MAR reconstruction significantly seems to improve both attenuation and noise adjacent to implants and in more distant areas (all p < 0.001) compared to standard ASIR V reconstructions alone. Signal-to-noise ratio (SNR; p = 0.001) and contrast-to-noise ratio were improved significantly (CNR; p = 0.001). Smart MAR improved visualization of tumor/abscess (detected in 36 of 100 patients, 36%) and representative oropharyngeal tissue (p < 0.001). In 8 of 36 patients (22%), tumor was only detected in Smart MAR series. Mean total DLP was 506.8mGy*cm; average CTDIvol was 5.5 mGy.

CONCLUSIONS

The supplementary use of the Smart MAR post-processing tool seems to significantly improve both subjective and objective image quality as well as diagnostic confidence and lesion detection in CT of the head and neck. In 22% of cases, the tumor was detected only in Smart MAR reconstructed images.

Association of Obesity with Quantitative Chest CT Measured Airway Wall Thickness in WTC Workers with Lower Airway Disease

BACKGROUND

We previously reported that wall area percent (WAP), a quantitative CT (QCT) indicator of airway wall thickness and, presumably, inflammation, is associated with adverse longitudinal expiratory flow trajectories in WTC workers, but that obesity and weight gain also seemed to be independently predictive of the latter. Previous studies have reported no association between WAP and obesity, so we investigated that association in nonsmoking WTC-exposed individuals and healthy unexposed controls.

METHODS

We assessed WAP using the Chest Imaging Platform QCT system in a segmental bronchus in 118 former WTC workers, and 89 COPDGene® WTC-unexposed and asymptomatic subjects. We used multiple regression to model WAP vs. body mass index (BMI) in the two groups, adjusting for important subject and CT image characteristics.

RESULTS

Unadjusted analyses revealed significant differences between the two groups with regards to WAP, age, gender, scan pixel spacing and slice interval, but not BMI or total lung capacity. In adjusted analysis, there was a significant interaction between BMI and WTC exposure on WAP. BMI was significantly and positively associated with WAP in the WTC group, but not in the COPDGene® group, but stratified analyses revealed that the effect was significant in WTC subjects with clinical evidence of lower airway disease (LAD).

DISCUSSION

Unlike non-diseased subjects, BMI was significantly associated with WAP in WTC workers and, in stratified analyses, the association was significant only among those with LAD. Our findings suggest that this adverse effect of obesity on airway structure and inflammation may be confined to already diseased individuals.

Image-based biomarkers for solid tumor quantification

The last few decades have witnessed tremendous technological developments in image-based biomarkers for tumor quantification and characterization. Initially limited to manual one- and two-dimensional size measurements, image biomarkers have evolved to harness developments not only in image acquisition technology but also in image processing and analysis algorithms. At the same time, clinical validation remains a major challenge for the vast majority of these novel techniques, and there is still a major gap between the latest technological developments and image biomarkers used in everyday clinical practice. Currently, the imaging biomarker field is attracting increasing attention not only because of the tremendous interest in cutting-edge therapeutic developments and personalized medicine but also because of the recent progress in the application of artificial intelligence (AI) algorithms to large-scale datasets. Thus, the goal of the present article is to review the current state of the art for image biomarkers and their use for characterization and predictive quantification of solid tumors. Beginning with an overview of validated imaging biomarkers in current clinical practice, we proceed to a review of AI-based methods for tumor characterization, such as radiomics-based approaches and deep learning.Key Points• Recent years have seen tremendous technological developments in image-based biomarkers for tumor quantification and characterization.• Image-based biomarkers can be used on an ongoing basis, in a non-invasive (or mildly invasive) way, to monitor the development and progression of the disease or its response to therapy.• We review the current state of the art for image biomarkers, as well as the recent developments in artificial intelligence (AI) algorithms for image processing and analysis.

Demystification of AI-driven medical image interpretation: past, present and future

The recent explosion of 'big data' has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-long advances in computational technology development for medical image analysis. In this article, we provide an overview of the history of AI methods for radiological image analysis in order to provide a context for the latest developments. We review the functioning, strengths and limitations of more classical methods as well as of the more recent deep learning techniques. We discuss the unique characteristics of medical data and medical science that set medicine apart from other technological domains in order to highlight not only the potential of AI in radiology but also the very real and often overlooked constraints that may limit the applicability of certain AI methods. Finally, we provide a comprehensive perspective on the potential impact of AI on radiology and on how to evaluate it not only from a technical point of view but also from a clinical one, so that patients can ultimately benefit from it. KEY POINTS: • Artificial intelligence (AI) research in medical imaging has a long history • The functioning, strengths and limitations of more classical AI methods is reviewed, together with that of more recent deep learning methods. • A perspective is provided on the potential impact of AI on radiology and on its evaluation from both technical and clinical points of view.

Motion-corrected multiparametric renal arterial spin labelling at 3 T: reproducibility and effect of vasodilator challenge

OBJECTIVES

We investigated the feasibility and reproducibility of free-breathing motion-corrected multiple inversion time (multi-TI) pulsed renal arterial spin labelling (PASL), with general kinetic model parametric mapping, to simultaneously quantify renal perfusion (RBF), bolus arrival time (BAT) and tissue T₁.

METHODS

In a study approved by the Health Research Authority, 12 healthy volunteers (mean age, 27.6 ± 18.5 years; 5 male) gave informed consent for renal imaging at 3 T using multi-TI ASL and conventional single-TI ASL. Glyceryl trinitrate (GTN) was used as a vasodilator challenge in six subjects. Flow-sensitive alternating inversion recovery (FAIR) preparation was used with background suppression and 3D-GRASE (gradient and spin echo) read-out, and images were motion-corrected. Parametric maps of RBF, BAT and T₁ were derived for both kidneys. Agreement was assessed using Pearson correlation and Bland-Altman plots.

RESULTS

Inter-study correlation of whole-kidney RBF was good for both single-TI (r² = 0.90), and multi-TI ASL (r² = 0.92). Single-TI ASL gave a higher estimate of whole-kidney RBF compared to multi-TI ASL (mean bias, 29.3 ml/min/100 g; p <0.001). Using multi-TI ASL, the median T₁ of renal cortex was shorter than that of medulla (799.6 ms vs 807.1 ms, p = 0.01), and mean whole-kidney BAT was 269.7 ± 56.5 ms. GTN had an effect on systolic blood pressure (p < 0.05) but the change in RBF was not significant.

CONCLUSIONS

Free-breathing multi-TI renal ASL is feasible and reproducible at 3 T, providing simultaneous measurement of renal perfusion, haemodynamic parameters and tissue characteristics at baseline and during pharmacological challenge.

KEY POINTS

• Multiple inversion time arterial spin labelling (ASL) of the kidneys is feasible and reproducible at 3 T. • This approach allows simultaneous mapping of renal perfusion, bolus arrival time and tissue T ₁ during free breathing. • This technique enables repeated measures of renal haemodynamic characteristics during pharmacological challenge.

Radiomics of liver MRI predict metastases in mice

Background

The purpose of this study was to investigate whether any texture features show a correlation with intrahepatic tumor growth before the metastasis is visible to the human eye.

Methods

Eight male C57BL6 mice (age 8–10 weeks) were injected intraportally with syngeneic MC-38 colon cancer cells and two mice were injected with phosphate-buffered saline (sham controls). Small animal magnetic resonance imaging (MRI) at 4.7 T was performed at baseline and days 4, 8, 12, 16, and 20 after injection applying a T2-weighted spin-echo sequence. Texture analysis was performed on the images yielding 32 texture features derived from histogram, gray-level co-occurrence matrix, gray-level run-length matrix, and gray-level size-zone matrix. The features were examined with a linear regression model/Pearson correlation test and hierarchical cluster analysis. From each cluster, the feature with the lowest variance was selected.

Results

Tumors were visible on MRI after 20 days. Eighteen features from histogram and the gray-level-matrices exhibited statistically significant correlations before day 20 in the experiment group, but not in the control animals. Cluster analysis revealed three distinct clusters of independent features. The features with the lowest variance were Energy, Short Run Emphasis, and Gray Level Non-Uniformity.

Conclusions

Texture features may quantitatively detect liver metastases before they become visually detectable by the radiologist.

DCE MRI reveals early decreased and later increased placenta perfusion after a stress challenge during pregnancy in a mouse model

OBJECTIVES

Stress during pregnancy is known to have negative effects on fetal outcome. The purpose of this exploratory study was to examine placental perfusion alterations after stress challenge during pregnancy in a mouse model.

MATERIAL AND METHODS

Seven Tesla MRI was performed on pregnant mice at embrionic day (ED) 14.5 and 16.5. Twenty dams were exposed to an established acoustic stress challenge model while twenty non-exposed dams served as controls. Placental perfusion was analyzed in dynamic contrast-enhanced (DCE) MRI using the steepest slope model. The two functional placental compartments, the highly vascularized labyrinth and the endocrine junctional zone, were assessed separately.

RESULTS

Statistical analysis revealed decreased perfusion levels in the stress group at ED 14.5 compared to controls in both placenta compartments. On ED 16.5, the perfusion level increased significantly in the stress group while placenta perfusion in controls remained similar or even slightly decreased leading to an overall increased perfusion in the stress group on ED 16.5 compared to controls.

CONCLUSION

MR imaging allows noninvasive placenta perfusion assessment in this fetal stress mimicking animal model. In this exploratory study, we demonstrated that stress challenge during pregnancy leads to an initial reduction followed by an increase of placenta perfusion.

Fully automated contour detection of the ascending aorta in cardiac 2D phase-contrast MRI

PURPOSE

In this study we proposed a fully automated method for localizing and segmenting the ascending aortic lumen with phase-contrast magnetic resonance imaging (PC-MRI).

MATERIAL AND METHODS

Twenty-five phase-contrast series were randomly selected out of a large population dataset of patients whose cardiac MRI examination, performed from September 2008 to October 2013, was unremarkable. The local Ethical Committee approved this retrospective study. The ascending aorta was automatically identified on each phase of the cardiac cycle using a priori knowledge of aortic geometry. The frame that maximized the area, eccentricity, and solidity parameters was chosen for unsupervised initialization. Aortic segmentation was performed on each frame using active contouring without edges techniques. The entire algorithm was developed using Matlab R2016b. To validate the proposed method, the manual segmentation performed by a highly experienced operator was used. Dice similarity coefficient, Bland-Altman analysis, and Pearson's correlation coefficient were used as performance metrics.

RESULTS

Comparing automated and manual segmentation of the aortic lumen on 714 images, Bland-Altman analysis showed a bias of -6.68mm², a coefficient of repeatability of 91.22mm², a mean area measurement of 581.40mm², and a reproducibility of 85%. Automated and manual segmentation were highly correlated (R=0.98). The Dice similarity coefficient versus the manual reference standard was 94.6±2.1% (mean±standard deviation).

CONCLUSION

A fully automated and robust method for identification and segmentation of ascending aorta on PC-MRI was developed. Its application on patients with a variety of pathologic conditions is advisable.

Adaptive statistical iterative reconstruction improves image quality without affecting perfusion CT quantitation in primary colorectal cancer

OBJECTIVES

To determine the effect of Adaptive Statistical Iterative Reconstruction (ASIR) on perfusion CT (pCT) parameter quantitation and image quality in primary colorectal cancer.

METHODS

Prospective observational study. Following institutional review board approval and informed consent, 32 patients with colorectal adenocarcinoma underwent pCT (100 kV, 150 mA, 120 s acquisition, axial mode). Tumour regional blood flow (BF), blood volume (BV), mean transit time (MTT) and permeability surface area product (PS) were determined using identical regions-of-interests for ASIR percentages of 0%, 20%, 40%, 60%, 80% and 100%. Image noise, contrast-to-noise ratio (CNR) and pCT parameters were assessed across ASIR percentages. Coefficients of variation (CV), repeated measures analysis of variance (rANOVA) and Spearman' rank order correlation were performed with statistical significance at 5%.

RESULTS

With increasing ASIR percentages, image noise decreased by 33% while CNR increased by 61%; peak tumour CNR was greater than 1.5 with 60% ASIR and above. Mean BF, BV, MTT and PS differed by less than 1.8%, 2.9%, 2.5% and 2.6% across ASIR percentages. CV were 4.9%, 4.2%, 3.3% and 7.9%; rANOVA P values: 0.85, 0.62, 0.02 and 0.81 respectively.

CONCLUSIONS

ASIR improves image noise and CNR without altering pCT parameters substantially.

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.

Creating normograms of dural sinuses in healthy persons using computer-assisted detection for analysis and comparison of cross-section dural sinuses in the brain

Dural sinuses vary in size and shape in many pathological conditions with abnormal intracranial pressure. Size and shape normograms of dural brain sinuses are not available. The creation of such normograms may enable computer-assisted comparison to pathologic exams and facilitate diagnoses. The purpose of this study was to quantitatively evaluate normal magnetic resonance venography (MRV) studies in order to create normograms of dural sinuses using a computerized algorithm for vessel cross-sectional analysis. This was a retrospective analysis of MRV studies of 30 healthy persons. Data were analyzed using a specially developed Matlab algorithm for vessel cross-sectional analysis. The cross-sectional area and shape measurements were evaluated to create normograms. Mean cross-sectional size was 53.27±13.31 for the right transverse sinus (TS), 46.87+12.57 for the left TS (p=0.089) and 36.65+12.38 for the superior sagittal sinus. Normograms were created. The distribution of cross-sectional areas along the vessels showed distinct patterns and a parallel course for the median, 25th, 50th and 75th percentiles. In conclusion, using a novel computerized method for vessel cross-sectional analysis we were able to quantitatively characterize dural sinuses of healthy persons and create normograms.

Novel mammographic image features differentiate between interval and screen-detected breast cancer: a case-case study

Background

Interval breast cancers are often diagnosed at a more advanced stage than screen-detected cancers. Our aim was to identify features in screening mammograms of the normal breast that would differentiate between future interval cancers and screen-detected cancers, and to understand how each feature affects tumor detectability.

Methods

From a population-based cohort of invasive breast cancer cases in Stockholm-Gotland, Sweden, diagnosed from 2001 to 2008, we analyzed the contralateral mammogram at the preceding negative screening of 394 interval cancer cases and 1009 screen-detected cancers. We examined 32 different image features in digitized film mammograms, based on three alternative dense area identification methods, by a set of logistic regression models adjusted for percent density with interval cancer versus screen-detected cancer as the outcome. Features were forward-selected into a multiple logistic regression model adjusted for mammographic percent density, age, BMI and use of hormone replacement therapy. The associations of the identified features were assessed also in a sample from an independent cohort.

Results

Two image features, ‘skewness of the intensity gradient’ and ‘eccentricity’, were associated with the risk of interval compared with screen-detected cancer. For the first feature, the per-standard deviation odds ratios were 1.32 (95 % CI: 1.12 to 1.56) and 1.21 (95 % CI: 1.04 to 1.41) in the primary and validation cohort respectively. For the second feature, they were 1.20 (95 % CI: 1.04 to 1.39) and 1.17 (95%CI: 0.98 to 1.39) respectively. The first feature was associated with the tumor size at screen detection, while the second feature was associated with the tumor size at interval detection.

Conclusions

We identified two novel mammographic features in screening mammograms of the normal breast that differentiated between future interval cancers and screen-detected cancers. We present a starting point for further research into features beyond percent density that might be relevant for interval cancer, and suggest ways to use this information to improve screening.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-016-0761-x) contains supplementary material, which is available to authorized users.

What Do We Measure in Oncology PET?

Positron emission tomography (PET) has come to the practice of oncology. It is known that ¹⁸F-fluorodeoxyglucose (FDG) PET is more sensitive for the assessment of treatment response than conventional imaging. In addition, PET has an advantage in the use of quantitative analysis of the study. Nowadays, various PET parameters are adopted in clinical settings. In addition, a wide range of factors has been known to be associated with FDG uptake. Therefore, there has been a need for standardization and harmonization of protocols and PET parameters. We will introduce PET parameters and discuss major issues in this review.

Optimal threshold of subtraction method for quantification of air-trapping on coregistered CT in COPD patients

OBJECTIVES

To investigate the optimal threshold of subtraction method for quantification of air trapping on co-registered CT in COPD patients in correlation with pulmonary function parameters.

METHODS

From June 2005 to October 2010, 174 patients were included in our study. Inspiration and expiration CT were performed followed by non-rigid registration using in-house software. The subtraction value per voxel between inspiration and registered expiration CT was obtained, and volume fraction of air trapping (air trapping index, ATI), using variable thresholds was calculated. ATI, expiration/inspiration ratio of mean lung density (E/I MLD) and the percentage of lung voxels below -856 HU on expiration CT (Exp-856) were correlated with FEF25-75% and RV/TLC.

RESULTS

The highest correlation coefficient with FEF25-75% was -0.656, using the threshold of 80 HU. As for RV/TLC, the highest correlation coefficient was 0.664, using the threshold of 30 HU. When plotting the relationship between subtraction thresholds and FEF25-75% and RV/TLC, the threshold of 60 HU was most suitable (r = -0.649 and 0.651). Those correlation coefficients were comparable to the results with E/I MLD (r = -0.670 and 0.657) and Exp-856 (r = -0.604 and 0.565).

CONCLUSIONS

The optimal threshold for quantification of air trapping was 60 HU and showed comparable correlations with pulmonary function parameters.

KEY POINTS

• The optimal CT threshold of subtraction method for air trapping was 60 HU. • ATI with 60 HU threshold was comparable to E/I MLD and Exp -856 . • Emphysema may substantially contribute to air trapping with statistical significance (P < 0.001).

Optimization of exposure in panoramic radiography while maintaining image quality using adaptive filtering

Objective The purpose of the present study was to investigate the potential of using advanced external adaptive image processing for maintaining image quality while reducing exposure in dental panoramic storage phosphor plate (SPP) radiography. Materials and methods Thirty-seven SPP radiographs of a skull phantom were acquired using a Scanora panoramic X-ray machine with various tube load, tube voltage, SPP sensitivity and filtration settings. The radiographs were processed using General Operator Processor (GOP) technology. Fifteen dentists, all within the dental radiology field, compared the structural image quality of each radiograph with a reference image on a 5-point rating scale in a visual grading characteristics (VGC) study. The reference image was acquired with the acquisition parameters commonly used in daily operation (70 kVp, 150 mAs and sensitivity class 200) and processed using the standard process parameters supplied by the modality vendor. Results All GOP-processed images with similar (or higher) dose as the reference image resulted in higher image quality than the reference. All GOP-processed images with similar image quality as the reference image were acquired at a lower dose than the reference. This indicates that the external image processing improved the image quality compared with the standard processing. Regarding acquisition parameters, no strong dependency of the image quality on the radiation quality was seen and the image quality was mainly affected by the dose. Conclusions The present study indicates that advanced external adaptive image processing may be beneficial in panoramic radiography for increasing the image quality of SPP radiographs or for reducing the exposure while maintaining image quality.

Visualization and quality assessment of the contrast transfer function estimation

The contrast transfer function (CTF) describes an undesirable distortion of image data from a transmission electron microscope. Many users of full-featured processing packages are often new to electron microscopy and are unfamiliar with the CTF concept. Here we present a common graphical output to clearly demonstrate the CTF fit quality independent of estimation software. Separately, many software programs exist to estimate the four CTF parameters, but their results are difficult to compare across multiple runs and it is all but impossible to select the best parameters to use for further processing. A new measurement is presented based on the correlation falloff of the calculated CTF oscillations against the normalized oscillating signal of the data, called the CTF resolution. It was devised to provide a robust numerical quality metric of every CTF estimation for high-throughput screening of micrographs and to select the best parameters for each micrograph. These new CTF visualizations and quantitative measures will help users better assess the quality of their CTF parameters and provide a mechanism to choose the best CTF tool for their data.

AutoTag and AutoSnap: Standardized, semi-automatic capture of regions of interest from whole slide images

Tumor angiogenesis is measured by counting microvessels in tissue sections at high power magnification as a potential prognostic or predictive biomarker. Until now, regions of interest (ROIs) were selected by manual operations within a tumor by using a systematic uniform random sampling (SURS) approach. Although SURS is the most reliable sampling method, it implies a high workload. However, SURS can be semi-automated and in this way contribute to the development of a validated quantification method for microvessel counting in the clinical setting. Here, we report a method to use semi-automated SURS for microvessel counting: •Whole slide imaging with Pannoramic SCAN (3DHISTECH)•Computer-assisted sampling in Pannoramic Viewer (3DHISTECH) extended by two self-written AutoHotkey applications (AutoTag and AutoSnap)•The use of digital grids in Photoshop(®) and Bridge(®) (Adobe Systems) This rapid procedure allows traceability essential for high throughput protein analysis of immunohistochemically stained tissue.

Trans-rectal ultrasound visibility of prostate lesions identified by magnetic resonance imaging increases accuracy of image-fusion targeted biopsies

PURPOSE

To compare the diagnostic yield of targeted prostate biopsy using image-fusion of multi-parametric magnetic resonance (mp-MR) with real-time trans-rectal ultrasound (TRUS) for clinically significant lesions that are suspicious only on mp-MR versus lesions that are suspicious on both mp-MR and TRUS.

METHODS

Pre-biopsy MRI and TRUS were each scaled on a 3-point score: highly suspicious, likely, and unlikely for clinically significant cancer (sPCa). Using an MR-TRUS elastic image-fusion system (Koelis), a 127 consecutive patients with a suspicious clinically significant index lesion on pre-biopsy mp-MR underwent systematic biopsies and MR/US-fusion targeted biopsies (01/2010-09/2013). Biopsy histological outcomes were retrospectively compared with MR suspicion level and TRUS-visibility of the MR-suspicious lesion. sPCa was defined as biopsy Gleason score ≥7 and/or maximum cancer core length ≥5 mm.

RESULTS

Targeted biopsies outperformed systematic biopsies in overall cancer detection rate (61 vs. 41 %; p = 0.007), sPCa detection rate (43 vs. 23 %; p = 0.0013), cancer core length (7.5 vs. 3.9 mm; p = 0.0002), and cancer rate per core (56 vs. 12 %; p < 0.0001), respectively. Highly suspicious lesions on mp-MR correlated with higher positive biopsy rate (p < 0.0001), higher Gleason score (p = 0.018), and greater cancer core length (p < 0.0001). Highly suspicious lesions on TRUS in corresponding to MR-suspicious lesion had a higher biopsy yield (p < 0.0001) and higher sPCa detection rate (p < 0.0001). Since majority of MR-suspicious lesions were also suspicious on TRUS, TRUS-visibility allowed selection of the specific MR-visible lesion which should be targeted from among the multiple TRUS suspicious lesions in each prostate.

CONCLUSIONS

MR-TRUS fusion-image-guided biopsies outperformed systematic biopsies. TRUS-visibility of a MR-suspicious lesion facilitates image-guided biopsies, resulting in higher detection of significant cancer.

Pilot study of low-dose nonenhanced computed tomography with iterative reconstruction for diagnosis of urinary stones

Purpose

To evaluate the efficacy of low-dose computed tomography (LDCT) for detecting urinary stones with the use of an iterative reconstruction technique for reducing radiation dose and image noise.

Materials and Methods

A total of 101 stones from 69 patients who underwent both conventional nonenhanced computed tomography (CCT) and LDCT were analyzed. Interpretations were made of the two scans according to stone characteristics (size, volume, location, Hounsfield unit [HU], and skin-to-stone distance [SSD]) and radiation dose by dose-length product (DLP), effective dose (ED), and image noise. Diagnostic performance for detecting urinary stones was assessed by statistical evaluation.

Results

No statistical differences were found in stone characteristics between the two scans. The average DLP and ED were 384.60±132.15 mGy and 5.77±1.98 mSv in CCT and 90.08±31.80 mGy and 1.34±0.48 mSv in LDCT, respectively. The dose reduction rate of LDCT was nearly 77% for both DLP and ED (p<0.01). The mean objective noise (standard deviation) from three different areas was 23.0±2.5 in CCT and 29.2±3.1 in LDCT with a significant difference (p<0.05); the slight increase was 21.2%. For stones located throughout the kidney and ureter, the sensitivity and specificity of LDCT remained 96.0% and 100%, with positive and negative predictive values of 100% and 96.2%, respectively.

Conclusions

LDCT showed significant radiation reduction while maintaining high image quality. It is an attractive option in the diagnosis of urinary stones.

Relationships between Tumor Volume and Lymphatic Metastasis and Prognosis in Early Oral Tongue Cancer

Objectives

Although T stage is an important prognostic tool for oral tongue cancer, it fails to define the depth of invasion and true three-dimensional volume of primary tumors. The purpose of this paper is to determine the relations between tumor volume and lymph node metastasis and survival in early oral tongue cancer.

Methods

Forty-seven patients with T1-2 tongue cancer were included. Tumor volumes were measured by the computerized segmentation of T2-weighted magnetic resonance imaging.

Results

The overall average tumor volume was 27.7 cm³ (range, 1.4 to 60.1 cm³). A significant positive correlation was found between tumor volume and pathological T stage, depth of invasion, and cervical lymph node metastasis (P<0.001, P<0.001, and P=0.002, respectively). When the tumor volume exceeded 20 cm³, the cervical metastasis rate increased to 69.2%. The overall 5-year disease specific survival rate was 80%. There was a statistically significant association between large tumor volume (≥20 cm³) and the 5-year disease-specific survival (P=0.046).

Conclusion

Tumor volume larger than 20 cm³ was associated with greater risk cervical lymph node metastasis and poor 5-year disease-specific survival rate in early oral tongue cancer patients.