Quantifying lung cancer heterogeneity using novel CT features: a cross-institute study.

BACKGROUND: Radiomics-based image metrics are not used in the clinic despite the rapidly growing literature. We selected eight promising radiomic features and validated their value in decoding lung cancer heterogeneity. METHODS: CT images of 236 lung cancer patients were obtained from three different institutes, whereupon radiomic features were extracted according to a standardized procedure. The predictive value for patient long-term prognosis and association with routinely used semantic, genetic (e.g., epidermal growth factor receptor (EGFR)), and histopathological cancer profiles were validated. Feature measurement reproducibility was assessed. RESULTS: All eight selected features were robust across repeat scans (intraclass coefficient range: 0.81-0.99), and were associated with at least one of the cancer profiles: prognostic, semantic, genetic, and histopathological. For instance, "kurtosis" had a high predictive value of early death (AUC at first year: 0.70-0.75 in two independent cohorts), negative association with histopathological grade (Spearman's r: - 0.30), and altered expression levels regarding EGFR mutation and semantic characteristics (solid intensity, spiculated shape, juxtapleural location, and pleura tag; all p < 0.05). Combined as a radiomic score, the features had a higher area under curve for predicting 5-year survival (train: 0.855, test: 0.780, external validation: 0.760) than routine characteristics (0.733, 0.622, 0.613, respectively), and a better capability in patient death risk stratification (hazard ratio: 5.828, 95% confidence interval: 2.915-11.561) than histopathological staging and grading. CONCLUSIONS: We highlighted the clinical value of radiomic features. Following confirmation, these features may change the way in which we approach CT imaging and improve the individualized care of lung cancer patients.

Imaging Features of Breast Periductal Stromal Tumor: A Case Report.

Breast periductal stromal tumor (PDST) is a rare biphasic tumor, with both benign ductal epithelium and non-phyllodes sarcomatous stroma. Its imaging features were rarely reported due to the rarity. In this study, we describe the case of a 48-year-old female who presented with a palpable mass in the right breast. Presurgery imaging evaluations of full-field digital mammography (FFDM), digital breast tomosynthesis (DBT), and ultrasonography (US) were performed. The imaging features include the following: 1. multiple solid lobulated lesions comprising nearly the entire right breast; 2. hypoechoic heterogeneous masses with internal separations and abundant blood flow; 3. FFDM and DBT showed multiple irregular high-density masses with lobulated margin, partially integrated. The patient underwent extended mastectomy of the right breast. The surgical pathology confirmed a PDST. After excision of the mass, she was followed up in the outpatient clinic for 25 months without local recurrence or distant metastasis.

Feasibility of utilizing ultra-low-dose contrast medium for pancreatic artery depiction using the combination of advanced virtual monoenergetic imaging and high-concentration contrast medium: an intra-patient study.

OBJECTIVES: The least amount of contrast medium (CM) should be used under the premise of adequate diagnosis. The purpose of this study is to evaluate the feasibility of utilizing ultra-low-dose (224 mgI/kg) CM for pancreatic artery depiction using the combination of advanced virtual monoenergetic imaging (VMI+) and high-concentration (400 mgI/mL) CM. MATERIALS AND METHODS: 41 patients who underwent both normal dose CM (ND-CM, 320 mgI/kg) and low dose CM (LD-CM, 224 mgI/kg) thoracoabdominal enhanced CT for tumor follow-up were prospectively included. The VMI+ at the energy level of 40-kev for LD-CM images was reconstructed. CT attenuation, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs) of the abdominal artery, celiac artery, and superior mesenteric artery (SMA) and qualitative scores of pancreatic arteries depiction were recorded and compared among the three groups (ND-CM, LD-CM, and VMI+ LD-CM images). ANOVA and Friedman tests were used for statistical analysis. RESULTS: All quantitative and qualitative parameters on LD-CM images were lower than that on ND-CM images (all p < 0.01). There were no significant differences of all arteries' qualitative scores between ND-CM and VMI+ LD-CM images (all p > 0.05). VMI+ LD-CM images had the highest mean CT and CNR values of all arteries (all p < 0.0001). The CM volume was 52.6 ± 9.4 mL for the ND-CM group and 37.0 ± 6.7 mL for the LD-CM group. CONCLUSION: Ultra-low-dose CM (224 mgI/kg) was feasible for depicting pancreatic arteries. Inferior angiographic image quality could be successfully compensated by VMI+ and high-concentration CM.

Comparison of image quality and lesion diagnosis in abdominopelvic unenhanced CT between reduced-dose CT using deep learning post-processing and standard-dose CT using iterative reconstruction: A prospective study.

PURPOSE: To compare image quality and lesion diagnosis between reduced-dose abdominopelvic unenhanced computed tomography (CT) using deep learning (DL) post-processing and standard-dose CT using iterative reconstruction (IR). METHOD: Totally 251 patients underwent two consecutive abdominopelvic unenhanced CT scans of the same range, including standard and reduced doses, respectively. In group A, standard-dose data were reconstructed by (blend 30 %) IR. In group B, reduced-dose data were reconstructed by filtered back projection reconstruction to obtain group B1 images, and post-processed using the DL algorithm (NeuAI denosing, Neusoft medical, Shenyang, China) with 50 % and 100 % weights to obtain group B2 and B3 images, respectively. Then, CT values of the liver, the second lumbar vertebral centrum, the erector spinae and abdominal subcutaneous fat were measured. CT values, noise levels, signal-to-noise ratios (SNRs), contrast-to-noise ratios (CNRs), radiation doses and subjective scores of image quality were compared. Subjective evaluations of low-density liver lesions were compared by diagnostic results from enhanced CT or Magnetic Resonance Imaging. RESULTS: Groups B3 and B1 showed the lowest and highest noise levels, respectively (P < 0.001). The SNR and CNR in group B3 were highest (P < 0.001). The radiation dose in group B was reduced by 71.5 % on average compared to group A. Subjective scores in groups A and B2 were highest (P < 0.001). Diagnostic sensitivity and confidence for liver metastases in groups A and B2 were highest (P < 0.001). CONCLUSIONS: Reduced-dose abdominopelvic unenhanced CT combined with DL post-processing could ensure image quality and satisfy diagnostic needs.

Optimizing the timing of diagnostic testing after positive findings in lung cancer screening: a proof of concept radiomics study.

BACKGROUND: The timeliness of diagnostic testing after positive screening remains suboptimal because of limited evidence and methodology, leading to delayed diagnosis of lung cancer and over-examination. We propose a radiomics approach to assist with planning of the diagnostic testing interval in lung cancer screening. METHODS: From an institute-based lung cancer screening cohort, we retrospectively selected 92 patients with pulmonary nodules with diameters ≥ 3 mm at baseline (61 confirmed as lung cancer by histopathology; 31 confirmed cancer-free). Four groups of region-of-interest-based radiomic features (n = 310) were extracted for quantitative characterization of the nodules, and eight features were proven to be predictive of cancer diagnosis, noise-robust, phenotype-related, and non-redundant. A radiomics biomarker was then built with the random survival forest method. The patients with nodules were divided into low-, middle- and high-risk subgroups by two biomarker cutoffs that optimized time-dependent sensitivity and specificity for decisions about diagnostic workup within 3 months and about repeat screening after 12 months, respectively. A radiomics-based follow-up schedule was then proposed. Its performance was visually assessed with a time-to-diagnosis plot and benchmarked against lung RADS and four other guideline protocols. RESULTS: The radiomics biomarker had a high time-dependent area under the curve value (95% CI) for predicting lung cancer diagnosis within 12 months; training: 0.928 (0.844, 0.972), test: 0.888 (0.766, 0.975); the performance was robust in extensive cross-validations. The time-to-diagnosis distributions differed significantly between the three patient subgroups, p < 0.001: 96.2% of high-risk patients (n = 26) were diagnosed within 10 months after baseline screen, whereas 95.8% of low-risk patients (n = 24) remained cancer-free by the end of the study. Compared with the five existing protocols, the proposed follow-up schedule performed best at securing timely lung cancer diagnosis (delayed diagnosis rate: < 5%) and at sparing patients with cancer-free nodules from unnecessary repeat screenings and examinations (false recommendation rate: 0%). CONCLUSIONS: Timely management of screening-detected pulmonary nodules can be substantially improved with a radiomics approach. This proof-of-concept study's results should be further validated in large programs.

Global evolution of research on pulmonary nodules: a bibliometric analysis.

Aim: To provide a historical and global picture of research concerning lung nodules, compare the contributions of major countries and explore research trends over the past 10 years. Methods: A bibliometric analysis of publications from Scopus (1970-2020) and Web of Science (2011-2020). Results: Publications about pulmonary nodules showed an enormous growth trend from 1970 to 2020. There is a high level of collaboration among the 20 most productive countries and regions, with the USA located at the center of the collaboration network. The keywords 'deep learning', 'artificial intelligence' and 'machine learning' are current hotspots. Conclusions: Abundant research has focused on pulmonary nodules. Deep learning is emerging as a promising tool for lung cancer diagnosis and management.

Clinical features of pulmonary cryptococcosis in thin-section CT in immunocompetent and non-AIDS immunocompromised patients.

RATIONAL AND OBJECTIVES: To compare thin-section computed tomography (CT) features of pulmonary cryptococcosis (PC) in immunocompetent and non-AIDS immunocompromised patients. MATERIALS AND METHODS: We retrospectively reviewed CT findings of 18 immunocompetent and 24 non-AIDS immunocompromised patients with clinically proven PC. Different patterns of pulmonary abnormalities between the two groups of patients were compared by Fisher's exact test. RESULTS: Pulmonary nodules were present in 37 of the 42 patients. Masses were detected in 16 patients and consolidation in 9. There were 12 patients with a solitary nodule or mass. Masses were associated with nodules in 12 patients. Consolidation was associated with nodules/masses in nine patients. The nodules/masses were associated with cavitations in 13 patients. Margination of nodules/masses was well defined in nine patients and ill-defined in 33. The abnormalities were predominantly distributed in the peripheral region of the lung (n = 29, 69.0%). The presence of cavitations in nodules/masses was significantly more frequent in non-AIDS immunocompromised than in immunocompetent patients (P = 0.001). CONCLUSIONS: The most common thin-section CT feature of PC was pulmonary nodules/masses, which were ill-defined and located peripherally. Cavitations within nodules/masses were more commonly found in non-AIDS immunocompromised patients. PC should be considered in the differential diagnosis of pulmonary nodules/masses.

Altered regional homogeneity in motor cortices in patients with multiple system atrophy.

Typical histopathological findings of multiple system atrophy (MSA) have been observed in motor related cortices, in addition to the common areas of the striatum, substantia nigra, olivopontocerebellar pathways, the intermediolateral cell columns of the spinal cord, and the cerebellum. The purpose of this study is to test the hypothesis that functional impairment associated with the histopathological findings exists in the motor cortex of MSA patients using resting-state fMRI. Twenty clinically probable MSA patients (9 with MSA-P and 11 with MSA-C subtype) and 11 healthy controls (HCs) were studied. The regional homogeneity (ReHo) approach was used to analyze low frequency spontaneous fluctuation of blood oxygen level dependent signal. Compared with the HCs, the MSA patients showed significantly decreased ReHo in the left primary sensorimotor cortex, posterior cingulate cortex, left lateral prefrontal cortex (PFC) and right inferior parietal lobule (IPL), together with increased ReHo in the right primary sensorimotor cortex, bilateral premotor cortices, bilateral supplementary motor areas, medial PFC and left IPL. The results support the hypothesis that motor cortex areas can be functionally involved and likely play a role in motor circuit dysfunction in MSA patients.

[Phase-weighted imaging in differentiating multiple system atrophy from idiopathic Parkinson disease].

OBJECTIVE: To assess the efficacy of phase-weighted imaging in differentiating multiple system atrophy (MSA) from idiopathic Parkinson disease (IPD). METHODS: A phase-weighted sequence was performed covering striatum in 21 patients with MSA, 20 patients with IPD, and 26 age-matched healthy volunteers after conventional brain magnetic resonance imaging examination on a 3.0T magnetic resonance imaging system. An experienced neuroradiologist who were blind to the clinical diagnosis applied region of interest (ROI) on post-processing phase images to measure the phase shift value of bilateral globus pallidus, putamen, and caudate head. RESULTS: The phase shift values at bilateral putamen of MSA group were significantly lower than those of IPD group and control group (P < 0.05), while phase shift values at other locations of MSA group were not statistically different from those of other groups. The phase shift values showed no statistical difference between IPD group and control group at all regions. CONCLUSION: Quantitative phase-weighted imaging is a useful sequence to reflect the abnormal iron accumulation in brains of patients with movement disorders and is helpful in the differential diagnosis between MSA and IPD.