Multisequence MRI-based radiomics signature as potential biomarkers for differentiating KRAS mutations in non-small cell lung cancer with brain metastases.

Background: Kirsten rat sarcoma virus (KRAS) has evolved from a genotype with predictive value to a therapeutic target recently. The study aimed to establish non-invasive radiomics models based on MRI to discriminate KRAS from epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) mutations in lung cancer patients with brain metastases (BM), then further explore the optimal sequence for prediction. Methods: This retrospective study involved 317 patients (218 patients in training cohort and 99 patients in testing cohort) who had confirmed of KRAS, EGFR or ALK mutations. Radiomics features were separately extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequences. The maximal information coefficient and recursive feature elimination method were used to select informative features. Then we built four radiomics models for differentiating KRAS from EGFR or ALK using random forest classifier. ROC curves were used to validate the capability of the models. Results: The four radiomics models for discriminating KRAS from EGFR all worked well, especially DWI and T2WI models (AUCs: 0.942, 0.942 in training cohort, 0.949, 0.954 in testing cohort). When KRAS compared to ALK, DWI and T2-FLAIR models showed excellent performance in two cohorts (AUCs: 0.947, 0.917 in training cohort, 0.850, 0.824 in testing cohort). Conclusions: Radiomics classifiers integrating MRI have potential to discriminate KRAS from EGFR or ALK, which are helpful to guide treatment and facilitate the discovery of new approaches capable of achieving this long-sought goal of cure in lung cancer patients with KRAS.

The incremental value of Mycobacterium tuberculosis trace nucleic acid detection in CT-guided percutaneous biopsy needle rinse solutions for the diagnosis of tuberculosis.

Introduction: Tuberculosis (TB) diagnosis still faces challenges with high proportion of bacteriologic test negative incidences worldwide. We assessed the diagnostic value of digital PCR (dPCR) analysis of ultramicro Mycobacterium tuberculosis (M.tb) nucleic acid in CT-guided percutaneous biopsy needle rinse solution (BNRS) for TB. Methods: BNRS specimens were consecutively collected and total DNA was purified. The concentrations of M.tb-specific IS6110 and IS1081 were quantified using droplet dPCR. The diagnostic performances of BNRS-dPCR and its sensitivity in comparison with conventional tests were analyzed. Results: A total of 106 patients were enrolled, 63 of whom were TB (48 definite and 15 clinically suspected TB) and 43 were non-TB. The sensitivity of BNRS IS6110 OR IS1081-dPCR for total, confirmed and clinically suspected TB was 66.7%, 68.8% and 60.0%, respectively, with a specificity of 97.7%. Its sensitivity was higher than that of conventional etiological tests, including smear microscopy, mycobacterial culture and Xpert using sputum and BALF samples. The positive detection rate in TB patients increased from 39.3% for biopsy AFB test alone to 73.2% when combined with BNRS-dPCR, and from 71.4% for biopsy M.tb molecular detection alone to 85.7% when combined with BNRS-dPCR. Conclusion: Our results preliminarily indicated that BNRS IS6110 OR IS1081-dPCR is a feasible etiological test, which has the potential to be used as a supplementary method to augment the diagnostic yield of biopsy and improve TB diagnosis.

A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer.

BACKGROUND: To establish a predictive model based on multisequence magnetic resonance imaging (MRI) using deep learning to identify wild-type (WT) epidermal growth factor receptor (EGFR), EGFR exon 19 deletion (19Del), and EGFR exon 21-point mutation (21L858R) simultaneously. METHODS: A total of 399 patients with proven brain metastases of non-small cell lung cancer (NSCLC) were retrospectively enrolled and divided into training (n = 306) and testing (n = 93) cohorts separately based on two timepoints. All patients underwent 3.0-T brain MRI including T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted imaging, and contrast-enhanced T1-weighted sequences. Radiomics features were extracted from each lesion based on four sequences. An algorithm combining radiomics approach with graph convolutional networks architecture (Radio-GCN) was designed for the prediction of EGFR mutation status and subtype. The area under the curve (AUC) at receiver operating characteristic analysis was used to evaluate the predication capabilities of each model. RESULTS: We extracted 1,290 radiomics features from each MRI sequence. The AUCs of the Radio-GCN model for identifying EGFR 19Del, 21L858R, and WT for the lesion-wise analysis were 0.996 ± 0.004, 0.971 ± 0.013, and 1.000 ± 0.000 on the independent testing cohort separately. It also yielded AUCs of 1.000 ± 0.000, 0.991 ± 0.009, and 1.000 ± 0.000 for predicting EGFR mutations respectively for the patient-wise analysis. The κ coefficients were 0.735 and 0.812, respectively. CONCLUSIONS: The constructed Radio-GCN model is a new potential tool to predict the EGFR mutation status and subtype in NSCLC patients with brain metastases. RELEVANCE STATEMENT: The study demonstrated that a deep learning approach based on multisequence MRI can help to predict the EGFR mutation status in NSCLC patients with brain metastases, which is beneficial to guide a personalized treatment. KEY POINTS: • This is the first study to predict the EGFR mutation subtype simultaneously. • The Radio-GCN model holds the potential to be used as a diagnostic tool. • This study provides an imaging surrogate for identifying the EGFR mutation subtype.

Chinese expert consensus on imaging diagnosis of drug-resistant pulmonary tuberculosis.

Tuberculosis (TB) remains one of the major infectious diseases in the world with a high incidence rate. Drug-resistant tuberculosis (DR-TB) is a key and difficult challenge in the prevention and treatment of TB. Early, rapid, and accurate diagnosis of DR-TB is essential for selecting appropriate and personalized treatment and is an important means of reducing disease transmission and mortality. In recent years, imaging diagnosis of DR-TB has developed rapidly, but there is a lack of consistent understanding. To this end, the Infectious Disease Imaging Group, Infectious Disease Branch, Chinese Research Hospital Association; Infectious Diseases Group of Chinese Medical Association of Radiology; Digital Health Committee of China Association for the Promotion of Science and Technology Industrialization, and other organizations, formed a group of TB experts across China. The conglomerate then considered the Chinese and international diagnosis and treatment status of DR-TB, China's clinical practice, and evidence-based medicine on the methodological requirements of guidelines and standards. After repeated discussion, the expert consensus of imaging diagnosis of DR-PB was proposed. This consensus includes clinical diagnosis and classification of DR-TB, selection of etiology and imaging examination [mainly X-ray and computed tomography (CT)], imaging manifestations, diagnosis, and differential diagnosis. This expert consensus is expected to improve the understanding of the imaging changes of DR-TB, as a starting point for timely detection of suspected DR-TB patients, and can effectively improve the efficiency of clinical diagnosis and achieve the purpose of early diagnosis and treatment of DR-TB.

Multisequence MRI-based radiomics analysis for early prediction of the risk of T790M resistance in new brain metastases.

Background: Predicting whether T790M emerges early is crucial to the adjustment of targeted drugs for non-small cell lung cancer (NSCLC) patients. This study aimed to evaluate the risk of T790M resistance in progressive new brain metastases (BMs) based on multisequence magnetic resonance imaging (MRI) radiomics. Methods: This retrospective study included 405 consecutive patients (training cohort: 294 patients; testing cohort: 111 patients) with proven NSCLC with disease progression of new BM. The radiomics features were separately extracted from T2-weighted imaging (T2WI), T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (T1-CE) sequence of baseline MRI. Then, we calculated radiomics scores (rad-score) of the 4 sequences respectively and established predictive models (lesion- or patient-level) to evaluate T790M resistance within up to 14 months using random forest classifier. Receiver operating characteristic (ROC) curves and F1 scores were used to validate the performance of two models in both the training and testing cohort. Results: There were significant differences in rad-scores of the four sequences between T790M-positive and negative groups whether in the training or testing cohort (P<0.05). The lesion-level model consisting of rad-scores showed excellent discrimination, with an area under the curve (AUC) and F1-score of 0.879 and 0.798 in the training cohort, and 0.834 and 0.742 in the testing cohort, respectively. The patient-level model also showed a favorable discriminatory ability with an AUC and F1 score of 0.851 and 0.837, which was confirmed with an AUC and F1 score of 0.734 and 0.716 in the testing cohort. Conclusions: The MRI-based radiomics signatures may be new markers to identify patients at high risk of developing resistance in the early period.

CT-based nomogram for early identification of T790M resistance in metastatic non-small cell lung cancer before first-line epidermal growth factor receptor-tyrosine kinase inhibitors therapy.

BACKGROUND: To evaluate the value of computed tomography (CT) radiomics in predicting the risk of developing epidermal growth factor receptor (EGFR) T790M resistance mutation for metastatic non-small lung cancer (NSCLC) patients before first-line EGFR-tyrosine kinase inhibitors (EGFR-TKIs) therapy. METHODS: A total of 162 metastatic NSCLC patients were recruited and split into training and testing cohort. Radiomics features were extracted from tumor lesions on nonenhanced CT (NECT) and contrast-enhanced CT (CECT). Radiomics score (rad-score) of two CT scans was calculated respectively. A nomogram combining two CT scans was developed to evaluate T790M resistance within up to 14 months. Patients were followed up to calculate the time of T790M occurrence. Models were evaluated by area under the curve at receiver operating characteristic analysis (ROC-AUC), calibration curve, and decision curve analysis (DCA). The association of the nomogram with the time of T790M occurrence was evaluated by Kaplan-Meier survival analysis. RESULTS: The nomogram constructed with the rad-score of NECT and CECT for predicting T790M resistance within 14 months achieved the highest ROC-AUCs of 0.828 and 0.853 in training and testing cohorts, respectively. The DCA showed that the nomogram was clinically useful. The Kaplan-Meier analysis showed that the occurrence time of T790M difference between the high- and low-risk groups distinguished by the rad-score was significant (p < 0.001). CONCLUSIONS: The CT-based radiomics signature may provide prognostic information and improve pretreatment risk stratification in EGFR NSCLC patients before EGFR-TKIs therapy. The multimodal radiomics nomogram further improved the capability. RELEVANCE STATEMENT: Radiomics based on NECT and CECT images can effectively identify and stratify the risk of T790M resistance before the first-line TKIs treatment in metastatic non-small cell lung cancer patients. KEY POINTS: • Early identification of the risk of T790M resistance before TKIs treatment is clinically relevant. • Multimodel radiomics nomogram holds potential to be a diagnostic tool. • It provided an imaging surrogate for identifying the pretreatment risk of T790M.

Multisequence MRI-based radiomics nomogram for early prediction of osimertinib resistance in patients with non-small cell lung cancer brain metastases.

Background: Osimertinib resistance is a major problem in the course of targeted therapy for non-small cell lung cancer (NSCLC) patients. To develop and validate a multisequence MRI-based radiomics nomogram for early prediction of osimertinib resistance in NSCLC with brain metastases (BM). Methods: Pretreatment brain MRI of 251 NSCLC patients proven with BM were retrospectively enrolled from two centers (training cohort: 196 patients; testing cohort: 55 patients). According to the gene test result of osimertinib resistance, patients were labeled as resistance and non-resistance groups (training cohort: 65 versus 131 patients; testing cohort: 25 versus 30 patients). Radiomics features were extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequences separately and radiomics score (rad-score) were built from the four sequences. Then a multisequence MRI-based nomogram was developed and the predictive ability was evaluated by ROC curves and calibration curves. Results: The rad-scores of the four sequences has significant differences between resistance and non-resistance groups in both training and testing cohorts. The nomogram achieved the highest predictive ability with area under the curve (AUC) of 0.989 (95 % confidence interval, 0.976-1.000) and 0.923 (95 % confidence interval, 0.851-0.995) in the training and testing cohort respectively. The calibration curves showed excellent concordance between the predicted and actual probability of osimertinib resistance using the radiomics nomogram. Conclusions: The multisequence MRI-based radiomics nomogram can be used as a noninvasive auxiliary tool to identify candidates who were resistant to osimertinib, which could guide clinical therapy for NSCLC patients with BM.

Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case-control study.

Objective: The purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling. Methods: A total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)>600 days n=152) noninert nodules (VDT<600 days n=49). Then taking the clinical imaging features obtained at the first examination as predictive variables the inert nodule judgement model >(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R2(determination coefficient). Results: The accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients' first examination and consultation. Conclusion: The INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volume-doubling time to accurately treat patients with pulmonary nodules.

Regional homogeneity alterations in multifrequency bands in patients with extracranial multi-organ tuberculosis: a prospective cross-sectional study.

Background: This study aimed to clarify the spontaneous neural activity in the conventional frequency band (0.01-0.08 Hz) and 2 subfrequency bands (slow-4: 0.027-0.073 Hz; slow-5: 0.01-0.027 Hz) in patients with extracranial multi-organ tuberculosis (EMTB) through regional homogeneity (ReHo) analysis. Methods: In all, 32 patients with EMTB and 31 healthy controls (HCs) were assessed by resting-state functional magnetic resonance imaging (rs-fMRI) scans to clarify the abnormal spontaneous neural activity through ReHo analysis in the conventional frequency band and 2 subfrequency bands. Results: Compared with the HCs, the patients with EMTB exhibited decreased ReHo in the left postcentral gyrus [t=-4.79; 95% confidence interval (CI): -0.79 to -0.31] and the left superior cerebellum (t=-4.45; 95% CI: -0.54 to -0.21) in the conventional band. Conversely, increased ReHo was observed in the right middle occipital gyrus (t=3.94; 95% CI: 0.18-0.53). In the slow-4 band, patients with EMTB only exhibited decreased ReHo in the superior cerebellum (t=-4.69; 95% CI: -0.54 to -0.22); meanwhile, in the slow-5 band, these patients exhibited decreased ReHo in the right postcentral gyrus (t=-3.76; 95% CI: -0.74 to -0.21) and the left superior cerebellum (t=-5.20, 95% CI: -0.72 to -0.31). After Bonferroni correction, no significant correlation was observed between the ReHo values in clusters showing significant between-group differences and cognitive test scores. Conclusions: ReHo showed abnormal synchronous neural activity in patients with EMTB in different frequency bands, which provides a novel understanding of the pathological mechanism of EMTB.

Differentiating peritoneal tuberculosis and peritoneal carcinomatosis based on a machine learning model with CT: a multicentre study.

PURPOSE: It is still a challenge to make early differentiation of peritoneal tuberculosis (PTB) and peritoneal carcinomatosis (PC) clinically as well as on imaging and laboratory tests. We aimed to develop a model to differentiate PTB from PC based on clinical characteristics and primary CT signs. METHODS: This retrospective study included 88 PTB patients and 90 PC patients (training cohort: 68 PTB patients and 69 PC patients from Beijing Chest Hospital; testing cohort: 20 PTB patients and 21 PC patients from Beijing Shijitan Hospital). The images were analyzed for omental thickening, peritoneal thickening and enhancement, small bowel mesentery thickening, the volume and density of ascites, and enlarged lymph nodes (LN). Meaningful clinical characteristics and primary CT signs comprised the model. ROC curve was used to validate the capability of the model in the training and testing cohorts. RESULTS: There were significant differences in the following aspects between the two groups: (1) age; (2) fever; (3) night sweat; (4) cake-like thickening of the omentum and omental rim (OR) sign; (5) irregular thickening of the peritoneum, peritoneal nodules, and scalloping sign; (6) large ascites; and (7) calcified and ring enhancement of LN. The AUC and F1 score of the model were 0.971 and 0.923 in the training cohort and 0.914 and 0.867 in the testing cohort. CONCLUSION: The model has the potential to distinguish PTB from PC and thus has the potential to be a diagnostic tool.

Predicting EGFR T790M Mutation in Brain Metastases Using Multisequence MRI-Based Radiomics Signature.

RATIONALE AND OBJECTIVES: Timely identifying T790M mutation for non-small cell lung cancer (NSCLC) patients with brain metastases (BM) is essential to adjust targeted treatment strategies. To develop and validate radiomics models based on multisequence MRI for differentiating patients with T790M resistance from no T790M mutation in BM and explore the optimal sequence for prediction. MATERIALS AND METHODS: This retrospective study enrolled 233 patients with proven of BM in NSCLC which included 95 with T790M and 138 without T790M from two hospitals as the training cohort and testing cohort separately. Radiomics features extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequence respectively. The most predictable features were selected based on the maximal information coefficient and Boruta method. Then four radiomics models were built to characterize T790M mutation by random forest classifier. ROC curves, F1 score and DCA curves were constructed to validate the capability and verify the performance of four models. RESULTS: The DWI model showed best performance with AUC and F1 score of 0.886 and 0.789 in the training cohort, 0.850 and 0.743 in the testing cohort. DCA curves also showed higher overall net benefit from the DWI model than from the remaining three models in the testing cohort. Other three models also had some classification power whether in the training or testing cohort, especially T2-FLAIR model. CONCLUSION: Multisequence MRI-based radiomics has potential to predict the emergence of EGFR T790M resistance mutations especially the radiomics signature based on DWI sequence.

Differentiating malignant pleural mesothelioma and metastatic pleural disease based on a machine learning model with primary CT signs: A multicentre study.

Rationale and Objectives: It is still a challenge to make confirming diagnosis of malignant pleural mesothelioma (MPM), especially differentiating from metastatic pleural disease (MPD). The aim of this study was to develop a model to distinguish MPM with MPD based on primary CT signs. Materials and methods: We retrospectively recruited 150 MPM patients and 147 MPD patients from two centers and assigned them to training (115 MPM patients and 113 MPD patients) and testing (35 MPM patients and 34 MPD patients) cohorts. The images were analyzed for pleural thickening, hydrothorax, lymphadenopathy, thoracic volume and calcified pleural plaque (CPP). The selected clinical characteristics and primary CT signs comprised the model by multivariate logistic regression in the training cohort. Then the model was tested on the external testing cohort. ROC curve and F1 score were used to validate the capability of the model in both two cohorts. Results: There were significant differences between two groups: (1) carcinoembryonic antigen (CEA); (2) nodular and mass pleural thickening; (3) the enhancement of pleura; (4) focal, diffuse and circumferential pleural thickening; (5) the thickest pleura; (6) thickening of diaphragmatic pleura; (7) multiple nodules and effusion of interlobar pleura; (8) hilar LN and ring enhancement of LN; (9) punctate and stipe CPP. The AUC and F1 score of the model were 0.970 and 0.857 in the training cohort, 0.955 and 0.818 in the testing cohort. Conclusion: The model holds promise for use as a diagnostic tool to distinguish MPM from MPD.

Differentiating EGFR from ALK mutation status using radiomics signature based on MR sequences of brain metastasis.

PURPOSE: More and more small brain metastases (BMs) in asymptomatic patients can be detected even prior to their primary lung cancer with the development of MRI. The aim of this study was to develop a predictive radiomics model to identify epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) mutation status in BM and explore the optimal MR sequence for predication. METHODS: This retrospective study included 186 patients with proven BM of lung cancer (training cohort: 70 patients with EGFR mutations and 65 patients with ALK rearrangements; testing cohort: 26 patients with EGFR mutations and 25 patients with ALK rearrangements). Radiomics features were separately extracted from contrast-enhanced T1-weighted imaging (T1-CE), T2 fluid-attenuated inversion recovery (T2-FLAIR) and T2WI sequences. The model for three MR sequences were constructed using a random forest classifier. ROC curves were used to validate the capability of the models in the training and testing cohorts. RESULTS: The AUCs of the T2-FLAIR model were significantly higher than those of the T1-CE model in training cohort (0.991 versus 0.954) and testing cohort (0.950 versus 0.867) and much higher than those of the T2WI model in training cohort (0.991 versus 0.880) and testing cohort (0.950 versus 0.731). Besides, the F1 scores of the T1-CE model were slightly higher than the T2-FLAIR model and much higher than the T2WI model in two cohorts. CONCLUSION: T2-FLAIR and T1-CE radiomics models that can be used as noninvasive tools for identifying EGFR and ALK mutation status are helpful to guide therapeutic strategies.

Long-Read Sequencing Annotation of the Transcriptome in DNA-PK Inactivated Cells.

The DNA-dependent protein kinase catalytic subunit (DNA-PKcs) with a Ku70/Ku80 heterodimer constitutes the intact DNA-PK kinase, which is an upstream component of the DNA repair machinery that signals the DNA damage, orchestrates the DNA repair, and serves to maintain genome integrity. Beyond its role in DNA damage repair, the DNA-PK kinase is also implicated in transcriptional regulation and RNA metabolism, with an illuminated impact on tumor progression and therapeutic responses. However, the efforts to identify DNA-PK regulated transcriptomes are limited by short-read sequencing to resolve the full complexity of the transcriptome. Therefore, we leveraged the PacBio Single Molecule, Real-Time (SMRT) Sequencing platform to study the transcriptome after DNA-PK inactivation to further underscore the importance of its role in diseases. Our analysis revealed additional novel transcriptome and complex gene structures in the DNA-PK inactivated cells, identifying 8,355 high-confidence new isoforms from 3,197 annotated genes and 523 novel genes. Among them, 380 lncRNAs were identified. We validated these findings using computational approaches and confirmatory transcript quantification with short-read sequencing. Several novel isoforms representing distinct splicing events have been validated through PCR experiments. Our analyses provide novel insights into DNA-PK function in transcriptome regulation and RNA metabolism.

Amplitude of low-frequency fluctuations in multiple-frequency bands in patients with intracranial tuberculosis: a prospective cross-sectional study.

Background: Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to study brain functional alteration, but there have been no reports of research regarding the application of rs-fMRI in intracranial tuberculosis. The purpose of this prospective, cross-sectional study was to investigate spontaneous neural activity at different frequency bands in patients with intracranial tuberculosis using rs-fMRI with amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) methods. Methods: The rs-fMRI data of 31 patients with intracranial tuberculosis and 30 gender-, age-, and education-matched healthy controls (HCs) were included. The ALFF and fALFF values in the conventional frequency band (0.01-0.08 Hz) and 2 sub-frequency bands (slow-4: 0.027-0.073 Hz; slow-5: 0.01-0.027 Hz) were calculated and compared between the groups. The resultant T-maps were corrected using the Gaussian random field (GRF) theory (voxel P<0.01, cluster P<0.05). Correlations between the ALFF and fALFF values and neurocognitive scores were assessed. Results: Compared with the HCs, patients with intracranial tuberculosis showed decreased ALFF in the right paracentral lobule (T=-4.69) in the conventional frequency band, in the right supplementary motor area (T=-4.85) in the slow-4 band, and in the left supplementary motor area (T=-3.76) in the slow-5 band. Compared to the slow-5 band, the voxels with decreased ALFF were spatially more extensive in the slow-4 band. Compared with HCs, patients with intracranial tuberculosis showed decreased fALFF in the opercular parts of the right inferior frontal gyrus (T=-4.50) and the left inferior parietal lobe (T=-4.86) and increased fALFF in the left inferior cerebellum (T=5.84) in the conventional frequency band. In the slow-4 band, fALFF decreased in the opercular parts of the right inferior frontal gyrus (T=-5.29) and right precuneus (T=-4.34). In the slow-5 band, fALFF decreased in the left middle occipital gyrus (T=-4.65) and right middle frontal gyrus (T=-5.05). Conclusions: Patients with intracranial tuberculosis showed abnormal intrinsic brain activity at different frequency bands, and ALFF abnormalities in different brain regions could be better detected in the slow-4 band. This preliminary study might provide new insights into understanding the pathophysiological mechanism in intracranial tuberculosis.

Unicentric Castleman disease: multidetector computed tomography classification with surgical and pathologic correlation.

BACKGROUND: This study aimed to correlate multidetector computed tomography (MDCT) classification of unicentric Castleman disease with the surgical and pathologic features. METHODS: The imaging manifestations of 63 cases of unicentric Castleman disease confirmed by pathology were retrospectively analyzed. Every patient underwent an MDCT examination. Classification based on imaging manifestations, surgical, histopathological, and imaging features were simultaneously reviewed and analyzed by two radiologists, with any disagreements resolved by consensus. RESULTS: Sixty-three patients with unicentric Castleman disease were divided into I-IV types by imaging manifestations: type I, single mass with smooth margin (n=5); type II, single mass with irregular or lobulated margin (n=33); type III, single invasive mass with blurred margin (n=20); and type IV, multiple fused masses (n=5). Thirty-eight cases of type I and type II were diagnosed as hyaline-vascular type by pathology after complete surgical resection; 20 cases were type III, in which eight cases were partially resected, 17 cases were pathologically diagnosed as hyaline-vascular type, and the remaining three cases were a mixed type. In five cases of type IV that could not be completely resected, four cases were hyaline-vascular type, and one case was plasma cell type. CONCLUSIONS: MDCT is an excellent tool for the detection and diagnosis of unicentric Castleman disease. Classification by MDCT features is a reliable method for evaluating tumor infiltration and growth mode, which helps surgical optimization.

Recent advances in functionalized upconversion nanoparticles for light-activated tumor therapy.

Upconversion nanoparticles (UCNPs) are a class of optical nanocrystals doped with lanthanide ions that offer great promise for applications in controllable tumor therapy. In recent years, UCNPs have become an important tool for studying the treatment of various malignant and nonmalignant cutaneous diseases. UCNPs convert near-infrared (NIR) radiation into shorter-wavelength visible and ultraviolet (UV) radiation, which is much better than conventional UV activated tumor therapy as strong UV-light can be damaging to healthy surrounding tissue. Moreover, UV light generally does not penetrate deeply into the skin, an issue that UCNPs can now address. However, the current studies are still in the early stage of research, with a long way to go before clinical implementation. In this paper, we systematically analysed recent advances in light-activated tumor therapy using functionalized UCNPs. We summarized the purpose and mechanism of UCNP-based photodynamic therapy (PDT), gene therapy, immunotherapy, chemo-therapy and integrated therapy. We believe the creation of functional materials based on UCNPs will offer superior performance and enable innovative applications, increasing the scope and opportunities for cancer therapy in the future.

A comparison of ultra-high-resolution CT target scan versus conventional CT target reconstruction in the evaluation of ground-glass-nodule-like lung adenocarcinoma.

BACKGROUND: The aim of this study was to determine whether the clinical value of scanned computed tomography (CT) images is higher when using ultra-high-resolution CT (U-HRCT) target scanning than conventional CT target reconstruction scanning in the evaluation of ground-glass-nodule (GGN)-like lung adenocarcinoma. METHODS: A total of 91 consecutive patients with isolated GGN-like lung adenocarcinoma were included in this study from April 2017 to June 2018. U-HRCT and conventional CT scans were conducted in all enrolled patients. Two experienced thoracic radiologists independently assessed image quality and made diagnoses. Based on the pathological results, the accuracies of U-HRCT target scanning and conventional CT target reconstruction for detecting morphological features on CT, including spiculation of GGNs, bronchial vascular bundles, solid components in the nodules, burr, vacuole, air bronchial signs, and fissure distortion, were calculated. All statistical analyses were performed using SPSS 17.0 software. Enumeration data were tested using the Chi-square test. A P value of <0.05 was considered statistically significant. RESULTS: When both techniques were compared with the pathological findings, the detection rate for CT images obtained using U-HRCT target scanning and conventional CT target reconstruction with regard to the spiculation of GGNs, bronchial vascular bundles, and solid components in the nodules were 78% vs. 61.5%, 72.5% vs. 54.9%, 65.9% vs. 49.5%, respectively. The presence of the spiculation of GGNs, bronchial vascular bundles, and solid components in the nodules in U-HRCT target scanning was significantly higher than that in conventional CT target reconstruction (all P<0.05). However, no significant difference was observed between the two techniques with regard to the burr, vacuole, air bronchial signs, and fissure distortion (all P>0.05). CONCLUSIONS: When viewing GGNs, the detection rate was higher for U-HRCT target scanning than for conventional CT target reconstruction, and this improvement significantly enhanced the diagnostic accuracy of early lung adenocarcinoma.

Dual source CT angiography in popliteal artery entrapment syndrome.

AIM: To evaluate the clinical value of dual source computed tomography (DSCT) angiography in the diagnosis and treatment for popliteal artery entrapment syndrome (PAES). MATERIALS AND METHODS: 8 patients with PAES were retrospectively reviewed. 64-slice dual source CT angiography was performed based on the following protocol: 100 mL of Iopamidol (370 mgI/mL) was injected at a rate of 3.5 mL/s and arterial phase images were obtained by using bolus tracking. Axial DSCT images and reconstructed images including multi-planar reconstruction (MPR), maximum intensity projection (MIP), volume rendering (VR) were collected and analysed. All patients underwent Doppler colour ultrasound examinations and surgeries. RESULTS: The popliteal artery and the neighbouring muscular structures were clearly shown on the axial images revealing the cause of the arterial entrapment. Furthermore, the site and length of the segmental occlusion and collateral developments were well demonstrated on reconstructed images. Characterisation and classification based on DSCT angiography were confirmed by surgeries. PAES was accurately diagnosed by DSCT angiography in all enrolled patients. In contrary, only 5 PAES cases were accurately diagnosed by ultrasound examination. CONCLUSION: DSCT angiography is a noninvasive and valuable tool in the diagnosis of PAES and plays an important role in the determination of treatment plans.