Prediction of Ablation Rate for High-Intensity Focused Ultrasound Therapy of Adenomyosis in MR Images Based on Multi-model Fusion.

This study aimed to develop a model based on radiomics and deep learning features to predict the ablation rate in patients with adenomyosis undergoing high-intensity focused ultrasound (HIFU) therapy. A total of 119 patients with adenomyosis who received HIFU therapy were retrospectively analyzed. Participants were included in the training and testing queues in a 7:3 ratio. Radiomics features were extracted from T2-weighted imaging (T2WI) images, and VGG-19 was used to extract advanced deep features. An ensemble model based on multi-model fusion for predicting the efficacy of HIFU in adenomyosis was proposed, which consists of four base classifiers and was evaluated using accuracy, precision, recall, F-score, and area under the receiver operating characteristic curve (AUC). The predictive performance of the combined model combining radiomics and deep learning features outperformed the radiomics and deep learning feature models alone, with accuracy of 0.848 and 0.814 in training and test sets, and AUC of 0.916 and 0.861, respectively. Compared with the base classifiers that make up the multi-model fusion model, the fusion model also exhibited better prediction performance. The fusion model incorporating both radiomics and deep learning features had certain predictive value for the ablation rate of adenomyosis under HIFU therapy and could help select patients with adenomyosis who would benefit from HIFU therapy.

Multiparametric MRI-based radiomics nomogram for identifying cervix-corpus junction cervical adenocarcinoma from endometrioid adenocarcinoma.

OBJECTIVE: To developed a magnetic resonance imaging (MRI) radiomics nomogram to identify adenocarcinoma at the cervix-corpus junction originating from the endometrium or cervix in order to better guide clinical treatment. METHODS: Between February 2011 and September 2021, the clinicopathological data and MRI in 143 patients with histopathologically confirmed cervical adenocarcinoma (CAC, n = 86) and endometrioid adenocarcinoma (EAC, n = 57) were retrospectively analyzed at the cervix-corpus junction. Radiomics features were extracted from fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, and delayed phase contrast-enhanced T1-weighted imaging (CE-T1WI) sequences. A radiomics nomogram was developed integrating radscore with independent clinical risk factors. The area under the curve (AUC) was used to evaluate the diagnostic efficacy of the radscore, nomogram and two different experienced radiologists in differentiating CAC from EAC at the cervix-corpus junction, and Delong test was applied to compare the differences of their diagnostic performance. RESULTS: In the training cohort, the AUC was 0.93 for radscore; 0.97 for radiomics nomograms; 0.85 and 0.86 for radiologists 1 and 2, respectively. Delong test showed that the differential efficacy of nomogram was significant better than those of radiologists in the training cohort (both P < 0.05). CONCLUSIONS: The nomogram based on radscore and clinical risk factors could better differentiate CAC from EAC at the cervix-corpus junction than radiologists, and preoperatively and non-invasively identify the origin of adenocarcinoma at the cervix-corpus junction, which facilitates clinicians to make individualized treatment decision.

Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics.

Objective: High-grade serous ovarian cancer (HGSOC) has the highest mortality rate among female reproductive system tumors. Accurate preoperative assessment is crucial for treatment planning. This study aims to develop multitask prediction models for HGSOC using radiomics analysis based on preoperative CT images. Methods: This study enrolled 112 patients diagnosed with HGSOC. Laboratory findings, including serum levels of CA125, HE-4, and NLR, were collected. Radiomic features were extracted from manually delineated ROI on CT images by two radiologists. Classification models were developed using selected optimal feature sets to predict R0 resection, lymph node invasion, and distant metastasis status. Model evaluation was conducted by quantifying receiver operating curves (ROC), calculating the area under the curve (AUC), De Long's test. Results: The radiomics models applied to CT images demonstrated superior performance in the testing set compared to the clinical models. The area under the curve (AUC) values for the combined model in predicting R0 resection were 0.913 and 0.881 in the training and testing datasets, respectively. De Long's test indicated significant differences between the combined and clinical models in the testing set (p = 0.003). For predicting lymph node invasion, the AUCs of the combined model were 0.868 and 0.800 in the training and testing datasets, respectively. The results also revealed significant differences between the combined and clinical models in the testing set (p = 0.002). The combined model for predicting distant metastasis achieved AUCs of 0.872 and 0.796 in the training and test datasets, respectively. The combined model displayed excellent agreement between observed and predicted results in predicting R0 resection, while the radiomics model demonstrated better calibration than both the clinical model and combined model in predicting lymph node invasion and distant metastasis. The decision curve analysis (DCA) for predicting R0 resection favored the combined model over both the clinical and radiomics models, whereas for predicting lymph node invasion and distant metastasis, DCA favored the radiomics model over both the clinical model and combined model. Conclusion: The identified radiomics signature holds potential value in preoperatively evaluating the R0, lymph node invasion and distant metastasis in patients with HGSC. The radiomics nomogram demonstrated the incremental value of clinical predictors for surgical outcome and metastasis estimation.

MRI Plain Scan: A Tool in the Management of Cervical Cancer during Pregnancy.

OBJECTIVE: The purpose of this study was to assess the diagnostic value of magnetic resonance imaging (MRI) in staging and treatment of cervical cancer in pregnancy, and to evaluate the benefit of apparent diffusion coefficient (ADC) during neoadjuvant chemotherapy management. MATERIALS AND METHODS: This was a retrospective cohort study. Patients were divided into two groups according to the stage of cervical cancer. The mean term of pregnancy at the time of the diagnosis was the early second trimester (range 10-27 weeks) and the median age was 33 years (range 26-40 years). The abdominal and pelvic MRI images and clinical data of these patients were reviewed. Tumor size, local tumor spread, and nodal involvement were evaluated using an MRI dataset. The treatment and follow-up imaging were analyzed as well, and the ADC was measured before and after the chemotherapy. RESULTS: 16 patients with histopathologically confirmed cervical cancer during pregnancy were retrospectively enrolled. 7 patients were diagnosed with local cervical cancer (FIGO stage IAI) and designated as early stage group, as the lesion was invisible on MRI. In this group, pregnancies were allowed to continue until cesarean delivery (CD) at 38-41 weeks. The other 9 patients presenting with local or extensive cervical cancer (FIGO stage IB2-IIA2) were designated as the advanced-stage group. The lesion could be measured and analyzed on MRI. They were treated with neoadjuvant chemotherapy in pregnancy. Among them, 6 patients underwent TP regimen (paclitaxel 135~175 mg/m2 plus cisplatin 70~75 mg/m2), while 3 patients received TC regimen (paclitaxel 135~175 mg/m2 plus carboplatin AUC=5). NACT was performed for 1 to 2 courses before surgery. ADC demonstrated significant differences before and after chemotherapy administered during pregnancy (1.06 ± 0.12 sec/mm2 vs. 1.34 ± 0.21 sec/mm2). CONCLUSION: MRI has been found to be helpful in staging cervical cancer in pregnancy. Patients with stage IA confirmed by MRI can choose conservative treatment and continue the pregnancy until term birth. MRI can dynamically monitor the efficacy of chemotherapy for patients with stage IB and above during pregnancy. ADC value can have a potential role in the evaluation of chemotherapy efficacy.

Complex Blood Supply Patterns in Cesarean Scar Pregnancy: Insights from Digital Subtraction Angiography Imaging.

BACKGROUND Understanding the blood supply pattern of cesarean scar pregnancy (CSP) can effectively help to determine the best choice of treatment. The aim of this study was to investigate the blood supply pattern and outcomes of patients with CSP through digital subtraction angiography (DSA) imaging. MATERIAL AND METHODS This was a retrospective cohort study. Patients were divided into 2 groups according to the type of CSP. The DSA images of these patients were reviewed, including the type of blood supply, dominant vessel, and collateral blood supply to the gestational sac. The clinical outcomes were analyzed between the 2 groups. RESULTS Thirty-seven patients with type I and 29 patients with type II CSP were enrolled in this study. Type II CSP showed a higher proportion of rich blood supply than type I (44.83% vs 29.72%, P>0.05). Compared with type II CSP, type I CSP tended to have bilateral dominant blood supply predominance (67.57% vs 41.38%, P<0.05). The incidence of collateral blood supply was 5.41% in the type I CSP group and 31.03% in the type II CSP group (P<0.05). In the type II CSP group, multiple collateral blood vessels were found in 4 patients. The superior vesicle artery was the most common source of collateral blood supply in both groups. Two patients with type II CSP suffered massive bleeding during surgery after uterine artery embolization (UAE). None of the patients received a hysterectomy. CONCLUSIONS UAE is safe and effective for both types of CSP. The blood supply pattern is more complex and abnormal in type II CSP. More attention should be paid to the collateral blood supply to achieve complete embolization during the UAE procedure in the case of type II CSP.

Post treatment and 3 month contrast enhanced MRI findings following HIFU of submucosal fibroids: a retrospective study.

PURPOSE: This study used contrast-enhanced MRI (CE-MRI) examination to assess the efficacy of high-intensity focused ultrasound (HIFU) for submucosal fibroids. METHODS: A total of 81 submucosal fibroids, including 33 cases of type 1, 29 cases of type 2, and 19 cases of type 2-5, treated by HIFU were retrospectively reviewed. CE-MRI was performed in all cases immediately after HIFU, the non-perfused volume ratio (NPVR) and the degree of endometrial impairment were recorded. Thereafter, CE-MRI was repeated in all cases after three months, and the change of fibroid volume shrinkage rate (FVSR), NPVR and the degree of endometrial impairment were recorded. RESULTS: The immediate NPVR was 86.4 ± 19.3% in type 1, 90.0 ± 13.3% in type 2 and 90.3 ± 7.2% in type 2-5. Among 81 fibroids, grades 0, 1, 2 and 3 endometrial impairments were observed in 38.3%, 16.1%, 14.8% and 30.9%, respectively. Three months later, NPVR was 68.0 ± 36.4% in type 1, 74.3 ± 27.7% in type 2 and 85.0 ± 16.1% in type 2-5. Grades 0, 1, 2 and 3 endometrial impairments were observed in 64.2%, 23.5%, 9.9% and 2.4%.FVSR was 49.0 ± 1.3% in type 1, 39.6 ± 1.7% in type 2 and 37.2 ± 2.1% in type 2-5. The FVSR in submucosal fibroid type 1 was superior to type 2 and type 2-5 (p < 0.05). The NPVR of submucosal fibroids in type 2-5 were higher than type 1 (p < 0.05) .There was no difference among different types of submucosal fibroids in endometrial impairment (p > 0.05) three months after HIFU. CONCLUSIONS: At three months after HIFU, FVSR was better for submucosal fibroid type 1 than for type 2 and type 2-5. And there was no difference in endometrial impairment among the different types of submucosal fibroid groups.

Validation of models in predicting residual disease in ovarian cancer: comparing CT urography with PET/CT.

BACKGROUND: Many ovarian cancer (OC) residual-disease prediction models were not externally validated after being constructed, the clinical applicability needs to be evaluated. PURPOSE: To compare computed tomography urography (CTU) with PET/CT in validating models for predicting residual disease in OC. MATERIAL AND METHODS: A total of 250 patients were included during 2018-2021. The CTU and PET/CT scans were analyzed, generating CT-Suidan, PET-Suidan, CT-Peking Union Medical College Hospital (PUMC), and PET-PUMC models. All imagings were evaluated by two readers independently, then compared to pathology. According to surgical outcomes, all patients were divided into the R0 group, with no visible residual disease, and the R1 group, with any visible residual disease. Logistic regression was used to assess the discrimination and calibration abilities of each model. RESULTS: CTU and PET/CT showed good diagnostic performance in predicting OC peritoneal metastases based on the Suidan and PUMC model (all the accuracies >0.8). As for model evaluation, the value of correct classification of the CT-Suidan, PET-Suidan, CT-PUMC, and PET-PUMC models was 0.89, 0.84, 0.88, and 0.83, respectively, representing stable calibration. The areas under the curve (AUC) of these models were 0.95, 0.90, 0.91, and 0.90, respectively. Furthermore, the accuracy of these models at the optimal threshold value (score 3) was 0.75, 0.78, 0.80, and 0.80, respectively. All two-paired comparisons of the AUCs and accuracies did not show a significant difference (all P > 0.05). CONCLUSION: CT-Suidan, CT-PUMC, PET-Suidan, and PET-PUMC models had equal abilities in predicting the residual disease of OC. The CT-PUMC model was recommended for its economic and user-friendly characteristics.

APOBEC3B stratifies ovarian clear cell carcinoma with distinct immunophenotype and prognosis.

BACKGROUND: Ovarian clear cell carcinoma (OCCC) is a challenging disease due to its intrinsic chemoresistance. Immunotherapy is an emerging treatment option but currently impeded by insufficient understanding of OCCC immunophenotypes and their molecular determinants. METHODS: Whole-genome sequencing on 23 pathologically confirmed patients was employed to depict the genomic profile of primary OCCCs. APOBEC3B expression and digital pathology-based Immunoscore were assessed by performing immunohistochemistry and correlated with clinical outcomes. RESULTS: An APOBEC-positive (APOBEC+) subtype was identified based on the characteristic mutational signature and prevalent kataegis events. APOBEC + OCCC displayed favourable prognosis across one internal and two external patient cohorts. The improved outcome was ascribable to increased lymphocytic infiltration. Similar phenomena of APOBEC3B expression and T-cell accumulation were observed in endometriotic tissues, suggesting that APOBEC-induced mutagenesis and immunogenicity could occur early during OCCC pathogenesis. Corroborating these results, a case report was presented for an APOBEC + patient demonstrating inflamed tumour microenvironment and clinical response to immune checkpoint blockade. CONCLUSIONS: Our findings implicate APOBEC3B as a novel mechanism of OCCC stratification with prognostic value and as a potential predictive biomarker that may inform immunotherapeutic opportunities.

CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer.

OBJECTIVE: We aimed to evaluate the prognostic value of C-C motif chemokine receptor type 5 (CCR5) expression level for patients with ovarian cancer and to establish a radiomics model that can predict CCR5 expression level using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) database. METHODS: A total of 343 cases of ovarian cancer from the TCGA were used for the gene-based prognostic analysis. Fifty seven cases had preoperative computed tomography (CT) images stored in TCIA with genomic data in TCGA were used for radiomics feature extraction and model construction. 89 cases with both TCGA and TCIA clinical data were used for radiomics model evaluation. After feature extraction, a radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. A prognostic scoring system incorporating radiomics signature based on CCR5 expression level and clinicopathologic risk factors was proposed for survival prediction. RESULTS: CCR5 was identified as a differentially expressed prognosis-related gene in tumor and normal sample, which were involved in the regulation of immune response and tumor invasion and metastasis. Four optimal radiomics features were selected to predict overall survival. The performance of the radiomics model for predicting the CCR5 expression level with 10-fold cross- validation achieved Area Under Curve (AUCs) of 0.770 and of 0.726, respectively, in the training and validation sets. A predictive nomogram was generated based on the total risk score of each patient, the AUCs of the time-dependent receiver operating characteristic (ROC) curve of the model was 0.8, 0.673 and 0.792 for 1-year, 3-year and 5-year, respectively. Along with clinical features, important imaging biomarkers could improve the overall survival accuracy of the prediction model. CONCLUSION: The expression levels of CCR5 can affect the prognosis of patients with ovarian cancer. CT-based radiomics could serve as a new tool for prognosis prediction.

A case report of lymphangioleiomyomatosis with retroperitoneal masses in pregnancy.

Background: Lymphangioleiomyomatosis (LAM) is a rare, gradually advancing tumor of unknown origin. It is distinguished by the anomalous proliferation of pulmonary smooth muscle cells and predominantly manifests in women of childbearing age. In this study, we aim to present a noteworthy case of LAM accompanied by lymphangioleiomyoma in the retroperitoneal space during pregnancy, a scenario susceptible to misdiagnosis. Case presentation: A 31-year-old woman, facing an unintended pregnancy, presented during the 13th week with a cystic-solid mass exhibiting abundant blood signals in the pelvic cavity, as revealed by routine obstetrical ultrasound. Concurrently, her chest CT disclosed diffuse thin-walled cavities in both lungs. Despite the absence of clinical symptoms, the patient abandoned pregnancy and underwent a complete curettage. However, 24 days post-operation, she was readmitted for further assessment, revealing an enlargement of the mass encompassing the abdominal aorta and inferior vena cava, along with compression on the middle and lower segments of the ureter. After a multi-disciplinary discussion and patient explanation, an exploratory laparotomy was performed, resulting in the complete removal of the tumor. Intraoperative pathological examination and immunohistochemical staining indicated a retroperitoneal mass devoid of malignant evidence. The comprehensive morphologic and immunophenotypic features substantiated the diagnosis of lymphangioleiomyomatosis. The postoperative course was uneventful, culminating in the patient's discharge. Conclusion: The consideration of Lymphangioleiomyomatosis (LAM) with a retroperitoneal tumor is crucial in the differential diagnosis of pelvic and abdominal masses. The preoperative diagnosis of this tumor poses a challenge, as ultrasound or CT scans may not yield definitive results. Accurate diagnosis necessitates not only a pathological examination of the retroperitoneal mass but also the correlation with the patient's chest High-Resolution Computed Tomography (HRCT) findings and corresponding clinical manifestations. Optimal management involves radical surgery, with surgeons comprehensively factoring in both fetal and maternal conditions when formulating a treatment plan.

Noninvasive evaluation of hypoxia in rabbit VX2 lung transplant tumors using spectral CT parameters and texture analysis.

AIM: Noninvasive evaluation of hypoxia in rabbit VX2 lung transplant tumors using spectral CT parameters and texture analysis. MATERIALS AND METHODS: Twenty-five VX2 lung transplant tumors of twenty-two rabbits were included in the study. Contrast-enhanced spectral CT scanning in the arterial phase (AP) and venous phase (VP) was performed. Tumors were divided into strong and weak hypoxic groups by hypoxic probe staining results. Spectral CT image-related parameters [70 keV CT value, normalized iodine concentration (NIC), slope of spectral HU curve (λHU)] were measured and the texture analysis on the monochromatic images was performed. Imaging parameters and texture features between tumors with different hypoxic degrees were compared and their diagnostic efficacies for predicting hypoxia in lung cancers were analyzed using receiver operating characteristic (ROC) curve. RESULTS: NIC in VP and λHU in VP of the strong hypoxic group were significantly higher than those in the weak hypoxic group (p < 0.05). For the texture features, entropy in VP and kurtosis in AP were significantly different between the two hypoxic groups. According to ROC analysis, λHU in VP had a better diagnostic ability for predicting hypoxia in tumors [Area Under Curve (AUC): 0.883, sensitivity: 85.7%, specificity: 100%]. The combination of four features improved AUC to 0.955. CONCLUSION: NIC in VP, λHU in VP, entropy in VP and kurtosis in AP have certain values in predicting tumor hypoxia and a combination of image parameters and texture features improves diagnostic efficiency.

Determining the invasiveness of ground-glass nodules using a 3D multi-task network.

OBJECTIVES: The aim of this study was to determine the invasiveness of ground-glass nodules (GGNs) using a 3D multi-task deep learning network. METHODS: We propose a novel architecture based on 3D multi-task learning to determine the invasiveness of GGNs. In total, 770 patients with 909 GGNs who underwent lung CT scans were enrolled. The patients were divided into the training (n = 626) and test sets (n = 144). In the test set, invasiveness was classified using deep learning into three categories: atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive pulmonary adenocarcinoma (IA). Furthermore, binary classifications (AAH/AIS/MIA vs. IA) were made by two thoracic radiologists and compared with the deep learning results. RESULTS: In the three-category classification task, the sensitivity, specificity, and accuracy were 65.41%, 82.21%, and 64.9%, respectively. In the binary classification task, the sensitivity, specificity, accuracy, and area under the ROC curve (AUC) values were 69.57%, 95.24%, 87.42%, and 0.89, respectively. In the visual assessment of GGN invasiveness of binary classification by the two thoracic radiologists, the sensitivity, specificity, and accuracy of the senior and junior radiologists were 58.93%, 90.51%, and 81.35% and 76.79%, 55.47%, and 61.66%, respectively. CONCLUSIONS: The proposed multi-task deep learning model achieved good classification results in determining the invasiveness of GGNs. This model may help to select patients with invasive lesions who need surgery and the proper surgical methods. KEY POINTS: • The proposed multi-task model has achieved good classification results for the invasiveness of GGNs. • The proposed network includes a classification and segmentation branch to learn global and regional features, respectively. • The multi-task model could assist doctors in selecting patients with invasive lesions who need surgery and choosing appropriate surgical methods.

Baseline immunity and impact of chemotherapy on immune microenvironment in cervical cancer.

BACKGROUND: We aimed to comprehensively evaluate the immunologic landscape at baseline and upon chemotherapy in cervical cancer. The information should aid ongoing clinical investigations of checkpoint blockade immunotherapies in this disease setting. METHODS: A series of 109 cervical carcinoma patients was retrospectively assayed before and after neoadjuvant chemotherapy. Tumour-infiltrating immune markers (CD3, CD4, CD8, CD20, CD56, CD68, PD-1, PD-L1) were assessed by immunohistochemistry. RNA sequencing analysis was performed on matched pre- and post-treatment fresh-frozen tissues. RESULTS: At diagnosis, diverse immune cell types including CD20+ B cells, CD3+ T cells, CD56+ natural killer (NK) cells, and CD68+ macrophages were detected in different proportions of cervical carcinoma. Unsupervised hierarchical clustering evidently showed that CD4+ and CD8+ T cell abundance correlated with PD-L1 expression. Based on the immune infiltration patterns, the patients could be stratified into four groups with prognostic relevance, namely, 'immuno-active', 'immuno-medial', 'immuno-NK', and 'immuno-deficient'. Neoadjuvant chemotherapy was associated with increased CD4, CD8, CD20, and CD56 signals, most prominently in good responders. Transcriptomic data corroborated the improved anticancer immunity and identified immunosuppressive CD200 upregulation following chemotherapeutic intervention. CONCLUSIONS: A subset of cervical cancer harbours active immune microenvironment, and chemotherapy treatment may further exert locoregional immunostimulation. Immune checkpoint inhibitors as combination or maintenance therapies warrant future exploration in clinic.

Niraparib as Maintenance Therapy in Germline ATM-mutated and Somatic BRCA2-mutated Ovarian Cancer with Brain Metastases: A Case Report and Literature Review.

Brain metastases from epithelial ovarian cancer are very rare with an incidence of only 1-2.5%. Many therapeutic methods such as surgery, irradiation and chemotherapy do produce survival benefits, but the overall outcome remains unsatisfactory. The BRCA (breast cancer susceptibility gene) mutation status seems to be associated with the development of brain metastases from ovarian cancer and these patients may benefit from treatment with PARP (poly ADP ribose polymerase) inhibitors. Here is a case where a Chinese female patient diagnosed with high-grade serous ovarian cancer with brain metastases was detected to have known germline ATM mutation and somatic BRCA2 mutation. The patient underwent whole brain radiotherapy and systemic chemotherapy, commenced niraparib as maintenance treatment and then presented considerable clinical and radiological response.

Impact of different scanners and acquisition parameters on robustness of MR radiomics features based on women's cervix.

MR Radiomics based on cervical lesions from one single scanner has achieved promising results. However, it is a challenge to achieve clinical translation. Considering multi-scanners and non-uniform scanning parameters from different centers in a real-world medical scenario, we should first identify the influence of such conditions on the robustness of MR radiomics features (RFs) based on the female cervix. In this study, 9 healthy female volunteers were enrolled and 3 kiwis were selected as references. Each of them underwent T2 weighted imaging in three different 3.0-T MR scanners with uniform acquisition parameters, and in one MR scanner with various scanning parameters. A total of 396 RFs were extracted from their images with and without decile intensity normalization. The RFs' reproducibility was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Representative features were selected using the hierarchical cluster analysis and their discrimination abilities were estimated by ROC analysis through retrospective comparison with the junctional zone and the outer muscular layer of healthy cervix in patients (n = 58) with leiomyoma. This study showed that only a few RFs were robust across different MR scanners and acquisition parameters based on females' cervix, which might be improved by decile intensity normalization method.

Determining the invasiveness of pure ground-glass nodules using dual-energy spectral computed tomography.

BACKGROUND: The present work aimed to investigate the clinical application of using quantitative parameters generated in the unenhanced phase (UP) and venous phase (VP) in dual-energy spectral CT for differentiating the invasiveness of pure ground-glass nodule (pGGN). METHODS: Sixty-two patients with 66 pGGNs who underwent preoperative dual-energy spectral CT in UP and VP were evaluated retrospectively. Nodules were divided into three groups based on pathology: adenocarcinoma in situ (AIS, n=19), minimally invasive adenocarcinoma (MIA, n=22) (both in the preinvasive lesion group) and invasive adenocarcinoma (IA, n=25). The iodine concentration (IC) and water content (WC) in nodules were measured in material decomposition images. The nodule CT numbers and slopes(k) were measured on monochromatic images. All measurements, including the maximum diameter of nodules were statistically compared between the AIS-MIA group and IA group. RESULTS: There were significant differences of WC in VP between AIS-MIA group and IA group (P<0.05). The CT attenuation values of the 40-140 keV monochromatic images in UP and VP were significantly higher for the invasive nodules. Logistic regression analysis showed that the maximum nodule diameter [odd ratio (OR) =1.21, 95% CI: 1.050-1.400, P<0.01] and CT number in 130 keV images in venous phase (OR =1.03, 95% CI: 1.014-1.047, P<0.001) independently predicted histological invasiveness. CONCLUSIONS: The quantitative parameters in dual-energy spectral CT in the unenhanced phase and venous phase provide useful information in differentiating preinvasive lesion group from IA group of pGGN, especially the maximum nodule diameter and CT number in the 130 keV images in the venous phase.

The associations among quantitative spectral CT parameters, Ki-67 expression levels and EGFR mutation status in NSCLC.

Dual-energy spectral computed tomography (DESCT) is based on fast switching between high and low voltages from view to view to obtain dual-energy imaging data, and it can generate monochromatic image sets, iodine-based material decomposition images and spectral CT curves. Quantitative spectral CT parameters may be valuable for reflecting Ki-67 expression and EGFR mutation status in non-small-cell lung cancer (NSCLC). We investigated the associations among the quantitative parameters generated in DESCT and Ki-67 expression and EGFR mutation in NSCLC. We studied sixty-five NSCLC patients with preoperative DESCT scans, and their specimens underwent Ki-67 and EGFR evaluations. Statistical analyses were performed to identify the spectral CT parameters for the diagnosis of Ki-67 expression and EGFR mutation status. We found that tumour grade and the slope of the spectral CT curve in the venous phase were the independent factors influencing the Ki-67 expression level, and the area under the curve (AUC) of the slope of the spectral CT curve in the venous phase in the receiver operating characteristic analysis for distinguishing different Ki-67 expression levels was 0.901. Smoking status and the normalized iodine concentration in the venous phase were independent factors influencing EGFR mutation, and the AUC of the two-factor combination for predicting the presence of EGFR mutation was 0.807. These results show that spectral CT parameters may be useful for predicting Ki-67 expression and the presence of EGFR mutation in NSCLC.

Construction of DOX/APC co-loaded BiOI@CuS NPs for safe and highly effective CT imaging and chemo-photothermal therapy of lung cancer.

Recently, a variety of nanoparticles have been widely used as imaging agents or carriers for the diagnosis and therapy of lung cancer. However, their poor imaging effect, high toxicity, pro-inflammatory effect and ineffective treatment are still a great challenge. In this work, we reported a novel kind of BiOI@CuS nanoparticle to achieve safe and effective therapy of lung cancer by co-loading hydrochloric acid doxorubicin (DOX) and aspirin phenacetin and caffeine (APC). The nanoparticles can effectively relieve inflammatory reactions induced by photo-thermal therapy (PTT). In vitro and in vivo assays showed that DOX/APC co-loaded BiOI@CuS exhibited an effective chemo-photothermal comprehensive therapy effect and good CT imaging capability. Consequently, this multifunctional nanosystem provides a versatile and promising platform in the imaging and treatment of lung cancer in further applications.