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Objective: To evaluate whether contrast-enhanced cone-beam breast CT (CE-CBBCT) features can risk-stratify prognostic stage in breast cancer. Methods: Overall, 168 biopsy-proven breast cancer patients were analysed: 115 patients in the training set underwent scanning using v. 1.5 CE-CBBCT between August 2019 and December 2019, whereas 53 patients in the test set underwent scanning using v. 1.0 CE-CBBCT between May 2012 and August 2014. All patients were restaged according to the American Joint Committee on Cancer eighth edition prognostic staging system. Following the combination of CE-CBBCT imaging parameters and clinicopathological factors, predictors that were correlated with stratification of prognostic stage via logistic regression were analysed. Predictive performance was assessed according to the area under the receiver operating characteristic curve (AUC). Goodness-of-fit of the models was assessed using the Hosmer-Lemeshow test. Results: As regards differentiation between prognostic stage (PS) I and II/III, increased tumour-to-breast volume ratio (TBR), rim enhancement pattern, and the presence of penetrating vessels were significant predictors for PS II/III disease (p < 0.05). The AUCs in the training and test sets were 0.967 [95% confidence interval (CI) 0.938–0.996; p < 0.001] and 0.896 (95% CI, 0.809–0.983; p = 0.001), respectively. Two features were selected in the training set of PS II vs III, including tumour volume [odds ratio (OR)=1.817, p = 0.019] and calcification (OR = 4.600, p = 0.040), achieving an AUC of 0.790 (95% CI, 0.636–0.944, p = 0.001). However, there was no significant difference in the test set of PS II vs III (P＞0.05). Conclusion: CE-CBBCT imaging biomarkers may provide a large amount of anatomical and radiobiological information for the pre-operative distinction of prognostic stage. Advances in knowledge: CE-CBBCT features have distinctive promise for stratification of prognostic stage in breast cancer.

Purpose To identify the optimal acquisition time to best discriminate between benign and malignant breast lesions on contrast-enhanced cone beam CT (CE-CBCT) and evaluate the potential of CE-CBCT to differentiate between breast cancer subtypes. Material and method A total of 98 women with a mean age of 49 ± 10 (SD) years (range: 29–77 years) with 100 BI-RADS 4 or 5 breast lesions were prospectively included. CE-CBCT images were obtained at 1- and 2-min after intravenous administration of iodinated contrast material. Contrast enhancement of breast lesions on CE-CBCT were evaluated and compared between different subtypes. Cut-off values for best discriminating between benign and malignant breast lesions with CE-CBCT were obtained from receiver operating characteristic curves. Results Malignant breast lesions showed greater enhancement than benign ones at 1-min (67.28 ± 39.79 [SD] HU vs. 42.27 ± 40.31 [SD] HU, respectively; P = 0.007) and 2-min (70.93 ± 38.05 [SD] HU vs. 48.94 ± 41.83 [SD] HU, respectively; P = 0.016) after intravenous administration of contrast material. At 1-min after intravenous administration of contrast material, an optimal cut-off value of 54.43 HU was found to best discriminate between malignant and benign breast lesions (AUC = 0.681; 95%CI: 0.558–0.805; P = 0.006) yielding 69.0% sensitivity (95%CI: 56.9–79.5%) and 69.2% specificity (95% CI: 48.2–85.7%). At 2-min, an optimal cut-off value of 72.65 HU was found to best discriminate between malignant and benign breast lesions (AUC = 0.654; 95%CI: 0.535–0.774; P = 0.020) yielding 50.7% sensitivity (95%CI: 38.6–62.8%) and 80.8% specificity (95%CI: 60.6–93.4%). CE-CBCT helped differentiate between immunohistochemical subtypes of breast lesions with lowest enhancement for triple negative lesions. No differences in enhancement were found among histopathological subtypes lesions at 1-min (P = 0.478) and 2-min (P = 0.625). Conclusion CE-CBCT helps discriminate between malignant and benign breast lesions, with best capabilities obtained at 1-min after intravenous administration of contrast material. For malignant lesions, quantitative analysis of enhancement on CE-CBCT helps differentiate between immunohistochemical subtypes.

Background. Response surveillance of neoadjuvant chemotherapy is needed to facilitate treatment decisions. We aimed to assess the imaging features of cone-beam breast computed tomography (CBBCT) for predicting the pathologic response of breast cancer after neoadjuvant chemotherapy. Methods. This prospective study included 81 women with locally advanced breast cancer who underwent neoadjuvant chemotherapy from August 2017 to January 2021. All patients underwent CBBCT before treatment, and 55 and 65 patients underwent CT examinations during the midtreatment (3 cycles) and late-treatment phases (7 cycles), respectively. Clinical information and quantitative parameters such as the diameter, volume, surface area, and CT density were compared between pathologic responders and nonresponders using the T –test and the Mann–Whitney U test. The performance of meaningful parameters was evaluated with the receiver operating characteristic curve, sensitivity, and specificity. Results. The quantitative results for the segmented volume, segmented surface area, segmented volume reduction, maximum enhancement ratio, wash-in rate and two-minute enhancement value in the mid- and late-treatment periods had predictive value for pathologic complete response. The area under the curve for the prediction model after multivariate regression analysis was 0.874. Conclusion. After comparing the outcomes of each timepoint, mid- and late-treatment parameters can be used to predict pathologic outcome. The late-treatment parameters showed significant value with a predictive model.

Background Breast density is an independent predictor of breast cancer risk. Quantitative volumetric breast density (QVBD) is expected to provide more information on the prediction of breast cancer risk. Purpose To evaluate the reliability of QVBD measurements based on cone-beam breast computed tomography (CBBCT) images. Material and Methods A total of 216 breasts were used to evaluate the stability of QVBD measurements based on CBBCT images and the correlations between this volumetric measurement and visual and area-based measurement methods. The intra- and inter-observer consistency of QVBD measurements were compared. Visual breast density (VBD) was evaluated with Breast Imaging Reporting and Data System (BI-RADS) standard on CBBCT images. The correlation between QVBD and VBD was evaluated by Spearman correlation coefficient. Receiver operating characteristic (ROC) curve was used to assess the sensitivity and specificity of the volumetric method in distinguishing dense and non-dense breasts. The correlation between QVBD and quantitative area-based breast density (QABD) was determined with Pearson correlation coefficient. Then, the breast volume measured with CBBCT images was compared with the breast specimen obtained during nipple-sparing mastectomy (NSM) by Pearson correlation coefficient and linear regression. Results Excellent intra- and inter-observer consistency was found from QVBD measurements. The volumetric method distinguished dense and non-dense breasts at a cutoff value of 9.5%, with 94.5% sensitivity and 77.1% specificity. Positive correlations were found between QVBD and QABD ( r=0.890; PConclusion CBBCT images can evaluate breast density reliably on a continuous scale.

Purpose: To develop and validate nomogram models using noninvasive imaging parameters with related clinical variables to predict the extent of axillary nodal involvement and stratify treatment options based on the essential cut-offs for axillary surgery according to the ACOSOG Z0011 criteria. Materials and Methods: From May 2007 to December 2017, 1799 patients who underwent preoperative breast and axillary magnetic resonance imaging (MRI) were retrospectively studied. Patients with data on axillary ultrasonography (AUS) were enrolled. The MRI images were interpreted according to Breast Imaging Reporting and Data system (BI-RADS). Using logistic regression analyses, nomograms were developed to visualize the associations between the predictors and each lymph node (LN) status endpoint. Predictive performance was assessed based on the area under the receiver operating characteristic curve (AUC). Bootstrap resampling was performed for internal validation. Goodness-of-fit of the models was evaluated using the Hosmer-Lemeshow test. Results: Of 397 early breast cancer patients, 200 (50.4%) had disease-free axilla, 119 (30.0%) had 1 or 2 positive LNs, and 78 (19.6%) had ≥3 positive LNs. Patient age, MRI features (mass margin, LN margin, presence/absence of LN hilum, and LN symmetry/asymmetry), and AUS descriptors (presence of cortical thickening or hilum) were identified as predictors of nodal disease. Nomograms with these predictors showed good calibration and discrimination; the AUC was 0.809 for negative axillary node (N0) vs. any LN metastasis, 0.749 for 1 or 2 involved nodes vs. N0, and 0.874 for ≥3 nodes vs. ≤2 metastatic nodes. The predictive ability of the 3 nomograms with additional pathological variables was significantly greater. Conclusion: The nomograms could predict the extent of ALN metastasis and facilitate decision-making preoperatively.

Purpose To utilize a neural architecture search (NAS) approach to develop a convolutional neural network (CNN) method for distinguishing benign and malignant lesions on breast cone-beam CT (BCBCT). Method 165 patients with 114 malignant and 86 benign lesions were collected by two institutions from May 2012 to August 2014. The NAS method autonomously generated a CNN model using one institution’s dataset for training (patients/lesions: 71/91) and validation (patients/lesions: 20/23). The model was externally tested on another institution’s dataset (patients/lesions: 74/87), and its performance was compared with fine-tuned ResNet-50 models and two breast radiologists who independently read the lesions in the testing dataset without knowing lesion diagnosis. Results The lesion diameters (mean ± SD) were 18.8 ± 12.9 mm, 22.7 ± 10.5 mm, and 20.0 ± 11.8 mm in the training, validation, and external testing set, respectively. Compared to the best ResNet-50 model, the NAS-generated CNN model performed three times faster and, in the external testing set, achieved a higher (though not statistically different) AUC, with sensitivity (95% CI) and specificity (95% CI) of 0.727, 80% (66–90%), and 60% (42–75%), respectively. Meanwhile, the performances of the NAS-generated CNN and the two radiologists’ visual ratings were not statistically different. Conclusions Our preliminary results demonstrated that a CNN autonomously generated by NAS performed comparably to pre-trained ResNet models and radiologists in predicting malignant breast lesions on contrast-enhanced BCBCT. In comparison to ResNet, which must be designed by an expert, the NAS approach may be used to automatically generate a deep learning architecture for medical image analysis.

BackgroundResidual cancer cells remaining after chemotherapy may have more aggressive behavior that promotes recurrence or metastasis, and which patients would benefit from subsequent additional treatment is controversial. The purpose of our study was to evaluate the prognostic value of the preoperative radiomics features of computed tomography (CT) imaging in breast cancer (BC) patients with residual tumors after neoadjuvant chemotherapy (NAC).MethodsPost-NAC CT images were reviewed from 114 patients who had received breast surgery and had residual breast tumors. The association of the 110 radiomics features derived from CT images with 5-year disease-free survival (DFS) was assessed by log-rank test in the training cohort, resulting in 13 prognostic radiomics features.ResultsWe constructed a radiomics signature consisting of four selected features by using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, which performed well in the discrimination with an area under the curve (AUC) of 0.78 (95% CI, 0.67–0.89) and 0.73 (95% CI, 0.59–0.87) in the training and validation cohorts, respectively. Radiomics nomogram, incorporating the radiomics signature with the conventional clinical variables, also performed well in the two cohorts (training cohort: AUC, 0.84; validation cohort: AUC, 0.82). Moreover, we found that the high-risk patients determined by our radiomics nomogram could benefit from postoperative adjuvant chemotherapy, while the low-risk and total patient groups could not.ConclusionsOur novel radiomics nomogram is a promising and favorable prognostic biomarker for preoperatively predicting survival outcomes and may aid in clinical decision-making in BC patients with residual tumors after NAC.

Background: This study aimed to develop and validate models to preoperatively predict the risk of the lymph node (LN) burden based on the Z0011 clinical trial to assist breast cancer surgical decision-making.Methods: Data on 1394 consecutive patients who presented at Sun Yat-sen University Cancer Center for Cone-beam breast computerized tomography (CBBCT) examinations between April 3, 2019, and July 17, 2020, were retrospectively collected. 387 patients who met the inclusion criteria were included and randomly divided into training and validation cohorts. Clinical-pathological information of all patients was recorded, and images were reinterpreted in this study. A bidirectional stepwise method followed by multi-variable analysis was used to incorporate preoperative features and build optimal model sets with the training cohort for prediction of N0 versus N+ and N＜3 versus N≥3.Results: The ROC curves of two models were generated with the training cohort, and their calibration abilities were estimated using 1000 bootstrap resamples. The bias-corrected C-index of the models were 0.779 (95% CI, 0.752–0.793) in model one and 0.809 (95% CI, 0.794–0.833) in model two for the training cohort. Decision curves and clinical impact curves were plotted to evaluate prediction performance for further clinical application. Delong’s test showed comparable performance of both cohorts.Conclusions: Our models were developed as reliable and noninvasive tools for the preoperative prediction of nodal status, and we hope that they can serve as useful tools for the early planning of treatment strategies for breast cancer patients.

Objectives: The diagnostic performance of intravoxel incoherent motion diffusion–weighted imaging (IVIM-DWI) in the differential diagnosis of breast tumors remains debatable among published studies. Therefore, this meta-analysis aimed to pool relevant evidence regarding the diagnostic performance of IVIM-DWI in the differential diagnosis of breast tumors.Methods: Studies on the differential diagnosis of breast lesions using IVIM-DWI were systemically searched in the PubMed, Embase and Web of Science databases in recent 10 years. The standardized mean difference (SMD) and 95% confidence intervals of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f) were calculated using Review Manager 5.3, and Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as assess publication bias and heterogeneity. Fagan's nomogram was used to predict the posttest probabilities.Results: Sixteen studies comprising 1,355 malignant and 362 benign breast lesions were included. Most of these studies showed a low to unclear risk of bias and low concerns regarding applicability. Breast cancer had significant lower ADC (SMD = −1.38, P < 0.001) and D values (SMD = −1.50, P < 0.001), and higher f value (SMD = 0.89, P = 0.001) than benign lesions, except D* value (SMD = −0.30, P = 0.20). Invasive ductal carcinoma showed lower ADC (SMD = 1.34, P = 0.01) and D values (SMD = 1.04, P = 0.001) than ductal carcinoma in situ. D value demonstrated the best diagnostic performance (sensitivity = 86%, specificity = 86%, AUC = 0.91) and highest post-test probability (61, 48, 46, and 34% for D, ADC, f, and D* values) in the differential diagnosis of breast tumors, followed by ADC (sensitivity = 76%, specificity = 79%, AUC = 0.85), f (sensitivity = 80%, specificity = 76%, AUC = 0.85) and D* values (sensitivity = 84%, specificity = 59%, AUC = 0.71).Conclusion: IVIM-DWI parameters are adequate and superior to the ADC in the differentiation of breast tumors. ADC and D values can further differentiate invasive ductal carcinoma from ductal carcinoma in situ. IVIM-DWI is also superior in identifying lymph node metastasis, histologic grade, and hormone receptors, and HER2 and Ki-67 status.

Objectives: Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is a promising non-invasive imaging technique to detect and grade prostate cancer (PCa). However, the results regarding the diagnostic performance of IVIM-DWI in the characterization and classification of PCa have been inconsistent among published studies. This meta-analysis was performed to summarize the diagnostic performance of IVIM-DWI in the differential diagnosis of PCa from non-cancerous tissues and to stratify the tumor Gleason grades in PCa.Materials and Methods: Studies concerning the differential diagnosis of prostate lesions using IVIM-DWI were systemically searched in PubMed, Embase, and Web of Science without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f). Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan's nomogram was used to predict the post-test probabilities.Results: Twenty studies with 854 patients confirmed with PCa were included. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. PCa showed a significantly lower ADC (SMD = −2.34; P < 0.001) and D values (SMD = −1.86; P < 0.001) and a higher D* value (SMD = 0.29; P = 0.01) than non-cancerous tissues, but no difference was noted with the f value (SMD = −0.16; P = 0.50). Low-grade PCa showed higher ADC (SMD = 0.63; P < 0.001) and D values (SMD = 0.80; P < 0.001) than the high-grade lesions. ADC showed comparable diagnostic performance (sensitivity = 86%; specificity = 86%; AUC = 0.87) but higher post-test probabilities (60, 53, 36, and 36% for ADC, D, D*, and f values, respectively) compared with the D (sensitivity = 82%; specificity = 82%; AUC = 0.85), D* (sensitivity = 70%; specificity = 70%; AUC = 0.75), and f values (sensitivity = 73%; specificity = 68%; AUC = 0.76).Conclusion: IVIM parameters are adequate to differentiate PCa from non-cancerous tissues with good diagnostic performance but are not superior to the ADC value. Diffusion coefficients can further stratify the tumor Gleason grades in PCa.

Background and objectives The diagnostic performance of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the differential diagnosis of pulmonary tumors remained debatable among published studies. This study aimed to pool and summary the relevant results to provide more robust evidence in this issue using a meta-analysis method. Materials and methods The researches regarding the differential diagnosis of lung lesions using IVIM-DWI were systemically searched in Pubmed, Embase, Web of science and Wangfang database without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f). Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan’s nomogram was used to predict the post-test probabilities. Results Eleven studies with 481 malignant and 258 benign lung lesions were included. Most include studies showed a low to unclear risk of bias and low concerns regarding applicability. Lung cancer demonstrated a significant lower ADC (SMD = -1.17, P P = 0.005) than benign lesions, except D* value (SMD = 0.01, P = 0.96). D value demonstrated the best diagnostic performance (sensitivity = 89%, specificity = 71%, AUC = 0.90) and highest post-test probability (57, 57, 43 and 43% for D, ADC, f and D* values) in the differential diagnosis of lung tumors, followed by ADC (sensitivity = 85%, specificity = 72%, AUC = 0.86), f (sensitivity = 71%, specificity = 61%, AUC = 0.71) and D* values (sensitivity = 70%, specificity = 60%, AUC = 0.66). Conclusion IVIM-DWI parameters show potentially strong diagnostic capabilities in the differential diagnosis of lung tumors based on the tumor cellularity and perfusion characteristics, and D value demonstrated better diagnostic performance compared to mono-exponential ADC.

To compare the comfort levels of cone beam breast computed tomography (CBBCT) and digital mammography.On 409 patients, CBBCT was performed within 1 week after conventional mammography. Patients evaluated their comfort by using an 11-point numerical rating scale (NRS-11) after completing the two examinations. The lower the score was, the more comfortable the examination modality was. The data was divided into different groups according to CBBCT scan mode (non-contrast mode, contrast-enhanced mode), age (≤44, 45∼59 and ≥60), body mass index (BMI) (18.5 kg/mOverall, the comfort of CBBCT was better than mammography (P0.05). CBBCT was more comfortable than mammography in both non-contrast and contrast-enhanced groups (P 0.05). CBBCT was more comfortable than mammography in the ≤44, 45∼59 age groups (P 0.05). There was no significant difference in the ≥60 age group (P = 0.5433). CBBCT was more comfortable than mammography in the BMI groups of 18.5∼23.9 kg/mThe comfort of CBBCT was better than mammography.

Purpose We aimed to investigate the feasibility of predicting invasion carcinoma from ductal carcinoma in situ (DCIS) lesions diagnosed by preoperative core needle biopsy using radiomics signatures, clinical imaging characteristics, and breast imaging reporting and data system (BI-RADS) descriptors on mammography. Methods Retrospectively, we enrolled 362 DCIS patients diagnosed by core needle biopsy, 110 (30.4%) of which had invasive carcinoma confirmed by operation and pathology. We analyzed the images identified suspicious calcification from 250 subjects (161 pure DCIS and 89 DCIS with invasion). A total of 569 calcification radiomics signatures were extracted from microcalcification for each patient. We included a group of routine clinical imaging characteristics and BI-RADS descriptors for comparison purpose. Five feature selection and seven classification methods were evaluated in terms of their prediction performance. We compared the area under the receiver operating characteristic curve (AUC) averaged from tenfold cross-validation of different feature sets to identify the best combination of feature selection and classification methods. Results Optimal feature selection and classification methods were identified after evaluating various combinations of feature sets. The best performance was achieved using both radiomics and clinical imaging characteristics (AUC = 0.72) performing better than BI-RADS descriptors or radiomics, but was no significant difference with clinical imaging characteristics (AUC = 0.66). The most significant features found were morphology signatures, first-order statistics, asymmetry/mass prevalence, and nuclear grade. Conclusions We found that the prediction model established using microcalcifications radiomics signatures and clinical imaging characteristics has the potential to identify an understaging of invasive breast cancer.

Diagnosis and staging of duodenal gastrointestinal stromal tumors (GISTs) by noninvasive imaging is critical for effective treatment, but the imaging features of duodenal GISTs remain largely undefined because of their rarity. The purpose of this article was to characterize duodenal GISTs using CT.Thirty-four patients with duodenal GISTs were analyzed by clinical symptom evaluation, pathologic examination, and CT in this retrospective study. Unenhanced and contrast-enhanced examinations were performed in all patients. Imaging characteristics, including the lesion location, size, growth pattern, ulceration, internal components, arterial blood supply, intratumoral arterioportal shunting, intratumoral vessels, rim enhancement, and enhancement patterns were reviewed.The duodenal GISTs were solitary masses with well-defined margins. The average diameter was 7.1 cm. The second portion was the most common site (20/34). Ulceration was a common feature (15/34). Calcification was uncommon (3/34), and mixed growth pattern was more common (26/34). Rim enhancement (24/34) and mixed enhancement pattern (15/34) were common. Arterial blood supply, intratumoral vasculature, and draining veins were all detected and were obvious on the arterial phase. The portal venous trunk and superior mesenteric vein were the main veins into which early arterioportal shunting drained.Primary duodenal GISTs are generally large, well-defined, heterogeneously enhancing, and hypervascular masses with a prominent mixed growth pattern on CT images. Our findings suggest that CT can help depict the origin of the tumoral arteries and draining veins on the arterial phase and may be a key defining diagnostic feature for duodenal GISTs.

Ultrasound-guided diffuse optical tomography (US-DOT) can potentially detect breast carcinomas by measuring total tumour haemoglobin concentrations (TTHC). The purpose of this study was to evaluate whether vascular haemoglobin concentrations (VHC) affect the ability of US-DOT to distinguish breast carcinomas from benign. In 85 women (97 palpable lesions) referred for core breast biopsy, we measured VHC with a complete blood count and calculated TTHCs for each lesion with US-DOT. Anaemia was defined as a VHC less than 120.0 g/L. Mean TTHCs were significantly higher in malignant lesions (n = 53) than in benign lesions (n = 44), regardless of whether the lesions were from women with anaemia (TTHC, 248.5 vs. 123.3 μmol/L; P = 0.001) or from those without (TTHC, 229.7 vs. 173.9 μmol/L; P = 0.016). A cut-off TTHC of 155.1 μmol/L provided 81.3 % sensitivity, 81.8 % specificity and 81.5 % accuracy for detecting malignant tumours in women with anaemia and 78.4 % sensitivity, 54.5 % specificity and 67.1 % accuracy for women without. There was no significant difference in sensitivity (P = 0.813), specificity (P = 0.108) and accuracy (P = 0.162) between the anaemic group and the non-anaemic group. Vascular haemoglobin concentrations did not affect the ability of US-DOT to differentiate breast carcinomas from benign lesions. • US-DOT can differentiate benign from malignant breast lesions by measuring TTHC. • No difference in TTHC between the anaemia and non-anaemia group. • Vascular haemoglobin concentrations do not affect the diagnostic ability of US-DOT.

Introduction The purpose of the study is to investigate the clinical value of F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in detecting the primary sites in patients with carcinoma of unknown primary (CUP). Methods The reports of FDG PET/CT scans of 164 patients with CUP syndrome in consecutive 2589 patients referred to our department from January 2006 to June 2010 were retrospectively reviewed. The final results were obtained from the pathologic reports, other imaging modalities diagnoses and clinical follow-up data. Results There were 142 cases in the results analysis, as 19 patients were lost to follow-up and three patients were excluded. FDG PET/CT successfully detected primary tumours in 67 (47.2%) out of 142 patients. Among this group, 53 were pathologically proved and 17 patients were confirmed by clinical follow-up. The primary sites of 38 (56.7%) were in lung, eight (11.9%) in nasopharynx and 13 (19.4%) in digestive system. Six patients were misdiagnosed by FDG PET/CT scan. FDG PET/CT could not detect the primary lesion in 66 patients, and three primary tumours were identified by conventional work-up after negative FDG PET/CT scan. The accuracy, sensitivity and specificity of FDG PET/CT scan in detecting the primary site in this study were 93.7%, 95.7% and 91.7%, respectively. FDG PET/CT scan changed the medical management of about 33.8% of 142 CUP patients. Conclusion FDG PET/CT whole-body imaging is a valuable tool in detecting the primary tumour of patients with CUP site.

Breast cancer occurs earlier in Chinese women than in Caucasian women. We have compared the clinicopathologic characteristics and prognosis in very young and older premenopausal women with breast cancer in south China. We separated 905 consecutive premenopausal patients with first diagnosis of breast cancer, surgically treated at the Sun Yat-sen University Cancer Center from October 2003 to December 2006, into a very young group (189 [13.7%]

Background The apparent diffusion coefficient (ADC) is a highly diagnostic factor in discriminating malignant and benign breast masses in diffusion-weighted magnetic resonance imaging (DW-MRI). The combination of ADC and other pictorial characteristics has improved lesion type identification accuracy. The objective of this study was to reassess the findings on an independent patient group by changing the magnetic field from 1.5-Tesla to 3.0-Tesla. Methods This retrospective study consisted of a training group of 234 female patients, including 85 benign and 149 malignant lesions, imaged using 1.5-Tesla MRI, and a test group of 95 female patients, including 19 benign and 85 malignant lesions, imaged using 3.0-Tesla MRI. The lesion of interest was segmented from the raw image and four sets of measurements describing the morphology, kinetics, DW-MRI, and texture of the pictorial properties of each lesion were obtained. Each lesion was characterized by 28 features in total. Three classical machine-learning algorithms were used to build prediction models on the training group, which evaluated the prognostic performance of the multi-sided features in three scenarios. To reduce information redundancy, five highly diagnostic factors were selected to obtain a compact yet informative characterization of the lesion status. Results Three classification models were built on the training of 1.5-Tesla patients and were tested on the independent 3.0-Tesla test group. The following results were found. i) Characterization of breast masses in a multi-sided way dramatically increased prediction performance. The usage of all features gave a higher performance in both sensitivity and specificity than any individual feature groups or their combinations. ii) ADC was a highly effective factor in improving the sensitivity in discriminating malignant from benign masses. iii) Five features, namely ADC, Sum Average, Entropy, Elongation, and Sum Variance, were selected to achieve the highest performance in diagnosis of the 3.0-Tesla patient group. Conclusions The combination of ADC and other multi-sided characteristics can increase the capability of discriminating malignant and benign breast lesions, even under different imaging protocols. The selected compact feature subsets achieved a high diagnostic performance and thus are promising in clinical applications for discriminating lesion type and for personalized treatment planning.

To assess the computed tomography (CT) findings of primary pulmonary lymphoepithelioma-like carcinoma (LELC).Clinical information and CT findings of 41 patients with pulmonary LELC were reviewed. CT images of 2 or 5mm thickness were obtained with a pre-treatment CT and were jointly evaluated by two radiologists.Thirty central tumors and 11 peripheral tumors with diameters ranging from 1.0 to 8.7cm (mean, 4.1±1.9cm) were identified. Central tumors appeared to be larger than peripheral tumors (P=0.017). Tumors occurred more frequently in right middle lobe (31.7%) and left lower lobe (29.3%). CT findings of patients with early stage were similar to those observed in patients with advanced stage except that lymphadenopathy was significantly more common in patients with advanced stage. CT findings of pulmonary LELC consisted of well defined border (63.4%), lobulation (78.0%), vascular or bronchial encasement (43.9%), obstructive pneumonia (41.5%), pleural effusion (12.2%) and calcification (4.9%). On contrast-enhanced CT scans, inhomogeneously enhanced tumors were significantly larger than homogeneously enhanced tumors (P0.001). Lymphadenopathy was seen in 28 patients, and lymph nodes with homogeneous enhancement were observed in 24/28 patients. Enlarged lymph nodes were more frequently occurred in peribronchial or hilar nodes (53.7%), subcrinal nodes (39.0%), right lower paratracheal nodes (31.7%) and right upper paratracheal nodes (22.0%).Pulmonary LELC usually appeared as a large, central, well defined and lobulated tumor with vascular or bronchial encasement and obstructive pneumonia. Calcification was rare in pulmonary LELC. Lymphadenopathy was common, usually with homogeneous enhancement.

Breast cancer occurs earlier in Chinese women than in Caucasian women. We have compared the clinicopathologic characteristics and prognosis in very young and older premenopausal women with breast cancer in south China. We separated 905 consecutive premenopausal patients with first diagnosis of breast cancer, surgically treated at the Sun Yat-sen University Cancer Center from October 2003 to December 2006, into a very young group (189 [13.7%];35 years old) and an older group (716 [52.0%]; 35-57 years old). Approximately, 90% of patients received adjuvant chemotherapy and hormonal therapy with tamoxifen for hormone-receptor (HR)-positive breast cancer. We retrospectively compared the clinicopathologic factors and survival rates of these two groups. The 3-year disease-free survival rate (78.0% versus 89.1%, p0.001) was poorer in the very young group, and 3-year overall survival rate (94.3% versus. 96.8%, p = 0.10) was similar. Moreover, the 3-year overall survival rate (p = 0.020) and disease-free survival rate (p0.001) were significantly poorer in HR-positive patients in the very young group whereas there was no significant difference in outcomes between the HR-negative groups. In China, age younger than 35 years is an independent predictor of breast cancer recurrence. In very young women with HR-positive breast cancer, chemotherapy plus tamoxifen alone may not be adequate standard treatment.

The purpose of the study is to investigate the clinical value of F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in detecting the primary sites in patients with carcinoma of unknown primary (CUP).The reports of FDG PET/CT scans of 164 patients with CUP syndrome in consecutive 2589 patients referred to our department from January 2006 to June 2010 were retrospectively reviewed. The final results were obtained from the pathologic reports, other imaging modalities diagnoses and clinical follow-up data.There were 142 cases in the results analysis, as 19 patients were lost to follow-up and three patients were excluded. FDG PET/CT successfully detected primary tumours in 67 (47.2%) out of 142 patients. Among this group, 53 were pathologically proved and 17 patients were confirmed by clinical follow-up. The primary sites of 38 (56.7%) were in lung, eight (11.9%) in nasopharynx and 13 (19.4%) in digestive system. Six patients were misdiagnosed by FDG PET/CT scan. FDG PET/CT could not detect the primary lesion in 66 patients, and three primary tumours were identified by conventional work-up after negative FDG PET/CT scan. The accuracy, sensitivity and specificity of FDG PET/CT scan in detecting the primary site in this study were 93.7%, 95.7% and 91.7%, respectively. FDG PET/CT scan changed the medical management of about 33.8% of 142 CUP patients.FDG PET/CT whole-body imaging is a valuable tool in detecting the primary tumour of patients with CUP site.

To compare quantitatively the enhanced thin CT section with pathologic findings in pulmonary carcinoma, pulmonary inflammatory pseudotumor (IPT) and pulmonary tuberculoma so as to demonstrate the relation of degree of enhancement and the vascular structure within the lesion with special emphasis on pulmonary carcinoma.Enhanced thin CT sections were obtained in 35 cases with nodular or patchy lesions in the peripheral lung field which are difficult to differentiate clinically. There were pulmonary carcinoma 21, inflammatory pseudotumor 7 and tuberculoma 7. The number of small vessels (inner diameter 0.02 approximately 0.1 mm), relatively large vessels (inner diameter0.1 mm) and their vascular bed areas were analyzed by computed image analyzing system. The relation between CT average attenuation and the number of vessels or the vascular bed areas were statistically evaluated.1. The differences of average attenuation in carcinoma, inflammatory pseudotumor and tuberculoma were statistically significant (P0.05). 2. The differences in number of small vessels, relatively large vessels and vascular bed areas among these three types of lesion were also significant (P0.05). 3. A positive correlation was found in the average CT affenuation of lung carcinoma and its number of small vessels and relatively large vessels and 4. A positive correlation was found between the average CT attenuation in these three lesions and the relatively large vessels, total vascular amount and vascular bed areas.1. The average degree of attenuation, being divided into four degrees, is of practical value in the differentiation of lung carcinoma, inflammatory pseudotumor and tuberculoma. 2. The average CT attenuation of lung carcinoma, inflammatory pseudotumor and tuberculoma is in direct proportion to the number of vessels and vessel bed areas and 3. The characteristic CT enhancement in lung carcinoma reflexes the condition of vessels and blood supply within the tumor.

Introduction Patients with nasopharyngeal carcinoma (NPC) sporadically develop abnormal adenoids. Nasopharyngeal adenoids are usually included in the gross tumor volume (GTV) but may have different therapeutic responses than tumor tissue. Therefore, distinguishing adenoids from tumor tissue may be required for precise and efficient chemoradiotherapy and radiotherapy. We characterized nasopharyngeal adenoids and investigated the therapeutic responses of NPC and nasopharyngeal adenoids using magnetic resonance imaging (MRI). Methods MRI data from 40 NPC patients with a coexisting adenoid mass before and after treatment were analyzed. The features of the adenoid masses, including location, striped appearance, size, interface, symmetry/asymmetry, and cysts, were evaluated. Treatment response were scored according to the World Health Organization guidelines. Results A striped appearance was observed in 36 cases before treatment and in all cases after treatment. In these 36 cases, the average GTVs including and excluding the uninvolved adenoids were 19.8 cm3 and 14.8 cm3, respectively. The average percentage change after excluding the uninvolved adenoids from the GTV was 31.0%. Stable disease in the adenoids was identified in 27 (96.4%) of 28 patients after neoadjuvant chemotherapy, while NPC clearly regressed. Partial adenoid responses were identified in 33 (82.5%) of 40 patients at 3 months after chemoradiotherapy or radiotherapy, whereas complete tumor responses were achieved in all patients. Six months after treatment, the adenoids continued to atrophy but did not disappear, and tumor recurrence was not found. Conclusions Nasopharyngeal adenoids and carcinoma tissue in NPC patients can be distinguished by using MRI and have different responses to chemoradiotherapy and radiotherapy. These findings contribute to better delineating the GTV of NPC, based on which spatially optimized strategies can be developed to render precise and efficient chemoradiotherapy and radiotherapy. Additionally, we observed a clear difference in the responses of these two tissue types to current therapies. This finding may reduce or avoid unnecessary biopsies or overtreatment.