MRI-based automatic identification and segmentation of extrahepatic cholangiocarcinoma using deep learning network.

BACKGROUND: Accurate identification of extrahepatic cholangiocarcinoma (ECC) from an image is challenging because of the small size and complex background structure. Therefore, considering the limitation of manual delineation, it's necessary to develop automated identification and segmentation methods for ECC. The aim of this study was to develop a deep learning approach for automatic identification and segmentation of ECC using MRI. METHODS: We recruited 137 ECC patients from our hospital as the main dataset (C1) and an additional 40 patients from other hospitals as the external validation set (C2). All patients underwent axial T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). Manual delineations were performed and served as the ground truth. Next, we used 3D VB-Net to establish single-mode automatic identification and segmentation models based on T1WI (model 1), T2WI (model 2), and DWI (model 3) in the training cohort (80% of C1), and compared them with the combined model (model 4). Subsequently, the generalization capability of the best models was evaluated using the testing set (20% of C1) and the external validation set (C2). Finally, the performance of the developed models was further evaluated. RESULTS: Model 3 showed the best identification performance in the training, testing, and external validation cohorts with success rates of 0.980, 0.786, and 0.725, respectively. Furthermore, model 3 yielded an average Dice similarity coefficient (DSC) of 0.922, 0.495, and 0.466 to segment ECC automatically in the training, testing, and external validation cohorts, respectively. CONCLUSION: The DWI-based model performed better in automatically identifying and segmenting ECC compared to T1WI and T2WI, which may guide clinical decisions and help determine prognosis.

Activation of p38 MAPK hinders the reactivation of visual cortical plasticity in adult amblyopic mice.

OBJECTIVE: To investigate the impact of p38 mitogen-activated protein kinase (MAPK) signaling on reactivating visual cortical plasticity in adult amblyopic mice. MATERIALS AND METHODS: Reverse suture (RS), environment enrichment (EE), and combined with left intracerebroventricular injection of p38 MAPK inhibitor (SB203580, SB) or p38 MAPK agonist (dehydrocorydaline hydrochloride, DHC) were utilized to treat adult amblyopic mice with monocular deprivation (MD). The visual water task, visual cliff test, and Flash visual-evoked potential were used to measure the visual function. Then, Golgi staining and transmission electron microscopy were used to assess the reactivation of structural plasticity in adult amblyopic mice. Western blot and immunohistochemistry detected the expression of ATF2, PSD-95, p38 MAPK, and phospho-p38 MAPK in the left visual cortex. RESULTS: No statistically significant difference was observed in the visual function in each pre-intervention group. Compared to pre-intervention, the visual acuity of deprived eyes was improved significantly, the impairment of visual depth perception was alleviated, and the P wave amplitude and C/I ratio were increased in the EE + RS, the EE + RS + SB, and the EE + RS + DMSO groups, but no significant difference was detected in the EE + RS + DHC group. Compared to EE + RS + DHC group, the density of dendritic spines was significantly higher, the synaptic density of the left visual cortex increased significantly, the length of the active synaptic zone increased, and the thickness of post-synaptic density (PSD) thickened in the left visual cortex of EE + RS, EE + RS + SB, and EE + RS + DMSO groups. And that, the protein expression of p-p38 MAPK increased while that of PSD-95 and ATF2 decreased significantly in the left visual cortex of the EE + RS + DHC group mice. CONCLUSION: RS and EE intervention improved the visual function and synaptic plasticity of the visual cortex in adult amblyopic mice. However, activating p38 MAPK hinders the recovery of visual function by upregulating the phosphorylation of p38 MAPK and decreasing the ATF2 protein expression.

Value of mammographic microcalcifications and MRI-enhanced lesions in the evaluation of residual disease after neoadjuvant therapy for breast cancer.

Background: Microcalcifications persist even if a patient with breast cancer achieves pathologic complete response (pCR) as confirmed by surgery after neoadjuvant treatment (NAT). In practice, surgeons tend to remove all the microcalcifications. This study aimed to explore the correlation between changes in the extent of microcalcification after NAT and pathological tumor response and compare the accuracy of mammography (MG) and magnetic resonance imaging (MRI) in predicting the size of residual tumors. Methods: This was a retrospective study which included a consecutive series of patients in Guangdong Provincial People's Hospital. Between January 2010 and January 2020, 127 patients with breast cancer and Breast Imaging Reporting and Data System (BI-RADS) 4-5 microcalcifications were included in this study. The maximum diameter of the microcalcifications on MG and lesion enhancement on MRI pre- and post-NAT were measured. The correlations between the changes in residual microcalcifications on MG and pCR were analyzed. Intraclass correlation coefficients (ICCs) were computed between the extent of the residual microcalcifications, residual enhancement, and residual tumor size. Results: There were no statistically significant differences in the changes in microcalcifications after NAT according to the RECIST criteria on MRI (P=0.09) and Miller-Payne grade (P=0.14). MRI showed a higher agreement than did residual microcalcifications on MG in predicting residual tumor size (ICC: 0.771 vs. 0.097). Conclusions: MRI is more accurate for evaluating residual tumor size in breast cancer. In our study, the extent of microcalcifications on MG after NAT had nearly no correlation with the pathological size of the residual tumor. Therefore, residual tumors with microcalcifications may not necessarily be a contraindication to breast-conserving surgery.

Regulatory role of the p38 MAPK/ATF2 signaling pathway in visual function and visual cortical plasticity in mice with monocular deprivation.

BACKGROUND: This study aimed to examine the role of the p38 mitogen-activated protein kinase (MAPK)/ activating transcription factor 2 (ATF2) signaling in visual function impairment and visual cortical plasticity in mice with monocular deprivation (MD). METHODS: Visual behavioral tests, including visual water task, visual cliff test, and flash visual evoked potential, were performed on each group. We studied the density of dendritic spines and the synaptic ultrastructure by Golgi staining and transmission electron microscope. We performed Western blot and immunohistochemistry and detected the expression of ATF2, PSD-95, p38 MAPK, and phosphor-p38 MAPK in the left visual cortex. RESULTS: In the MD + SB group, the visual acuity in deprived eyes substantially improved, the impairment of visual depth perception was alleviated, and the P wave amplitude and C/I ratio increased. The density of dendritic spines and the numerical density of synapses increased significantly, the width of the synaptic cleft decreased significantly, and the length of the active synaptic zone and the thickness of post-synaptic density (PSD) increased substantially. The protein expression of phosphor-p38 MAPK decreased, whereas that of PSD-95 and ATF2 increased significantly. CONCLUSIONS: Inhibiting the phosphorylation of p38 MAPK and negative feedback upregulated ATF2 expression, alleviated damage to visual function, and protected against synaptic plasticity in mice with MD.

L-distance ratio: a new distance ratio-based evaluation method for the diagnosis of cirrhosis using enhanced computed tomography.

Background: Early detection of liver cirrhosis is of great significance to the formulation of treatment plans and improving prognosis. Computed tomography (CT) is commonly used in the assessment of patients with chronic liver disease. In this study, we proposed a new distance ratio method for accurate diagnosis of cirrhosis using CT images. Methods: This was a retrospective study of a consecutive series of patients in Guangdong Provincial People's Hospital. Sixty-two patients with pathologically diagnosed cirrhosis but whose morphologic changes were insufficient to diagnose cirrhosis were included in the cirrhosis group. Those who were pathologically confirmed to be free of cirrhosis and fibrosis and without a history of chronic hepatic were classified as the control group. A total of 124 patients underwent abdominal dynamic enhanced CT. Both the L-distance ratio-the ratio of the distance from the right portal vein bifurcation point to the anterior and posterior edges of the liver-and the caudate-right lobe ratio were measured by two independent radiologists. Intraclass correlation coefficients (ICCs) were used to assess the agreement between the radiologists. Binary logistic regression was performed for univariate analysis, and the odds ratio (OR) was also calculated. The discrimination ability of the two methods was evaluated by the area under the receiver operating characteristic curve (AUC). Results: For both the L-distance ratio and the caudate-right lobe ratio, high agreement was observed between the two radiologists, although the ICC value of the L-distance ratio was slightly higher than that of the caudate-right lobe ratio (0.916 vs. 0.907). Binary logistic regression suggested that higher ratios were correlated with cirrhosis [the L-distance ratio, high vs. low OR =4.41, 95% confidence interval (CI): 2.08-9.36, P<0.001; the caudate-right lobe ratio, high vs. low OR =2.19, 95% CI: 1.07-4.49, P=0.031]. The AUCs of the L-distance ratio and the caudate-right lobe ratio were 0.823 (95% CI: 0.752-0.894) and 0.663 (95% CI: 0.569-0.757), respectively. Conclusions: The L-distance ratio method proposed in this paper is more simple, accurate, and reliable than the caudate-right lobe ratio method in the diagnosis of cirrhosis.

Using Machine Learning Methods to Assess Lymphovascular Invasion and Survival in Breast Cancer: Performance of Combining Preoperative Clinical and MRI Characteristics.

BACKGROUND: Preoperative assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) is of high clinical relevance for treatment decision-making and prognosis. PURPOSE: To investigate the associations of preoperative clinical and magnetic resonance imaging (MRI) characteristics with LVI and disease-free survival (DFS) by using machine learning methods in patients with IBC. STUDY TYPE: Retrospective. POPULATION: Five hundred and seventy-five women (range: 24-79 years) with IBC who underwent preoperative MRI examinations at two hospitals, divided into the training (N = 386) and validation datasets (N = 189). FIELD STRENGTH/SEQUENCE: Axial fat-suppressed T2-weighted turbo spin-echo sequence and dynamic contrast-enhanced with fat-suppressed T1-weighted three-dimensional gradient echo imaging. ASSESSMENT: MRI characteristics (clinical T stage, breast edema score, MRI axillary lymph node status, multicentricity or multifocality, enhancement pattern, adjacent vessel sign, and increased ipsilateral vascularity) were reviewed independently by three radiologists. Logistic regression (LR), eXtreme Gradient Boosting (XGBoost), k-Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms were used to establish the models by combing preoperative clinical and MRI characteristics for assessing LVI status in the training dataset, and the methods were further applied in the validation dataset. The LVI score was calculated using the best-performing of the four models to analyze the association with DFS. STATISTICAL TESTS: Chi-squared tests, variance inflation factors, receiver operating characteristics (ROC), Kaplan-Meier curve, log-rank, Cox regression, and intraclass correlation coefficient were performed. The area under the ROC curve (AUC) and hazard ratios (HR) were calculated. A P-value <0.05 was considered statistically significant. RESULTS: The model established by the XGBoost algorithm had better performance than LR, SVM, and KNN models, achieving an AUC of 0.832 (95% confidence interval [CI]: 0.789, 0.876) in the training dataset and 0.838 (95% CI: 0.775, 0.901) in the validation dataset. The LVI score established by the XGBoost model was an independent indicator of DFS (adjusted HR: 2.66, 95% CI: 1.22-5.80). DATA CONCLUSION: The XGBoost model based on preoperative clinical and MRI characteristics may help to investigate the LVI status and survival in patients with IBC. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Axillary ultrasound after neoadjuvant therapy reduces the false-negative rate of sentinel lymph node biopsy in patients with cytologically node-positive breast cancer.

OBJECTIVES: This study aimed to determine whether post-neoadjuvant therapy (NAT) axillary ultrasound (AUS) could reduce the false-negative rate (FNR) of sentinel lymph node biopsy (SLNB). We also performed subgroup analyses to identify the appropriate patient for SLNB. METHODS: A total of 220 patients with cytologically proven axillary node-positive breast cancer who underwent both SLNB and axillary lymph node dissection (ALND) after NAT were included. We calculated the FNR of SLNB. In the case of post-NAT AUS results available, AUS was classified as negative or positive. Then the FNR of post-NAT AUS combined with SLNB was evaluated. Subgroup analyses based on the number of sentinel lymph nodes removed, molecular subtypes, and the clinical N stage were also performed. RESULTS: The overall axillary lymph node pathological complete response rate was 45.5% (100/220). The FNR of SLNB alone was 15.8% (95%CI: 9.2 to 22.5%). Post-NAT AUS results were available for 181 patients. When combined negative post-NAT AUS results and SLNB, the FNR was reduced to 7.5% (95%CI: 2.4 to 12.7%). Subgroup analyses of the FNR for SLNB alone and negative post-NAT AUS combined with SLNB were shown as follows: in cases patients with less than three sentinel lymph nodes (SLNs) and at least three SLNs removed, the FNR was decreased from 24.5 to 13.2%, and 9.0 to 5.0%, respectively. The FNR was decreased from 20.8 to 10.5% in HR+/HER2+subgroup, 21.4 to 16.7% in HR-/HER2+subgroup, 15.9 to 7.0% in HR+/HER2- subgroup, and 0% in HR-/HER2- subgroup, respectively. For cN1 patients, the FNR was decreased from 18.1 to 12.1% while 17.1 to 3.6% for cN2 patients and 0% for cN3 patients. CONCLUSION: Using negative post-NAT AUS may help to decrease the FNR and improve patient selection for SLNB.

An integrated deep learning model for the prediction of pathological complete response to neoadjuvant chemotherapy with serial ultrasonography in breast cancer patients: a multicentre, retrospective study.

BACKGROUND: The biological phenotype of tumours evolves during neoadjuvant chemotherapy (NAC). Accurate prediction of pathological complete response (pCR) to NAC in the early-stage or posttreatment can optimize treatment strategies or improve the breast-conserving rate. This study aimed to develop and validate an autosegmentation-based serial ultrasonography assessment system (SUAS) that incorporated serial ultrasonographic features throughout the NAC of breast cancer to predict pCR. METHODS: A total of 801 patients with biopsy-proven breast cancer were retrospectively enrolled from three institutions and were split into a training cohort (242 patients), an internal validation cohort (197 patients), and two external test cohorts (212 and 150 patients). Three imaging signatures were constructed from the serial ultrasonographic features before (pretreatment signature), during the first-second cycle of (early-stage treatment signature), and after (posttreatment signature) NAC based on autosegmentation by U-net. The SUAS was constructed by subsequently integrating the pre, early-stage, and posttreatment signatures, and the incremental performance was analysed. RESULTS: The SUAS yielded a favourable performance in predicting pCR, with areas under the receiver operating characteristic curve (AUCs) of 0.927 [95% confidence interval (CI) 0.891-0.963] and 0.914 (95% CI 0.853-0.976), compared with those of the clinicopathological prediction model [0.734 (95% CI 0.665-0.804) and 0.610 (95% CI 0.504-0.716)], and radiologist interpretation [0.632 (95% CI 0.570-0.693) and 0.724 (95% CI 0.644-0.804)] in the external test cohorts. Furthermore, similar results were also observed in the early-stage treatment of NAC [AUC 0.874 (0.793-0.955)-0.897 (0.851-0.943) in the external test cohorts]. CONCLUSIONS: We demonstrate that autosegmentation-based SAUS integrating serial ultrasonographic features throughout NAC can predict pCR with favourable performance, which can facilitate individualized treatment strategies.

MRI characteristics of breast edema for assessing axillary lymph node burden in early-stage breast cancer: a retrospective bicentric study.

OBJECTIVES: To investigate whether breast edema characteristics at preoperative T2-weighted imaging (T2WI) could help evaluate axillary lymph node (ALN) burden in patients with early-stage breast cancer. METHODS: This retrospective study included women with clinical T1 and T2 stage breast cancer and preoperative MRI examination in two independent cohorts from May 2014 to December 2020. Low (< 3 LNs+) and high (≥ 3 LNs+) pathological ALN (pALN) burden were recorded as endpoint. Breast edema score (BES) was evaluated at T2WI. Univariable and multivariable analyses were performed by the logistic regression model. The added predictive value of BES was examined utilizing the area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). RESULTS: A total of 1092 patients were included in this study. BES was identified as the independent predictor of pALN burden in primary (n = 677) and validation (n = 415) cohorts. The analysis using MRI-ALN status showed that BES significantly improved the predictive performance of pALN burden (AUC: 0.65 vs 0.71, p < 0.001; IDI = 0.045, p < 0.001; continuous NRI = 0.159, p = 0.050). These results were confirmed in the validation cohort (AUC: 0.64 vs 0.69, p = 0.009; IDI = 0.050, p < 0.001; continuous NRI = 0.213, p = 0.047). Furthermore, BES was positively correlated with biologically invasive clinicopathological factors (p < 0.05). CONCLUSIONS: In individuals with early-stage breast cancer, preoperative MRI characteristics of breast edema could be a promising predictor for pALN burden, which may aid in treatment planning. KEY POINTS: • In this retrospective study of 1092 patients with early-stage breast cancer from two cohorts, the MRI characteristic of breast edema has independent and additive predictive value for assessing axillary lymph node burden. • Breast edema characteristics at T2WI positively correlated with biologically invasive clinicopathological factors, which may be useful for preoperative diagnosis and treatment planning for individual patients with breast cancer.

Preoperative prediction of intra-tumoral tertiary lymphoid structures based on CT in hepatocellular cancer.

PURPOSE: Intra-tumoral tertiary lymphoid structures (TLSs) are associated with a favorable prognosis for patients with hepatocellular carcinoma (HCC). We aimed to identify image features related to TLSs and develop a nomogram for preoperative noninvasive prediction of intra-tumoral TLSs. METHODS: This retrospective study enrolled patients with HCC who underwent contrast-enhanced computed tomography before surgery between January 2014 and September 2020. Two radiologists retrospectively and independently reviewed the CT imaging features, and interobserver agreement was assessed. Univariable and multivariable logistic regression analyses were applied to investigate clinical laboratory data and imaging features related to TLSs. A regression-based predictive model and nomogram were constructed using the identified predictors. Nomogram diagnostic performance was assessed with the area under the receiver operating characteristic curve (AUC) and calibration curves, and validated using 5-fold cross-validation. RESULTS: Ninety-three of the 142 HCCs were TLS + HCCs. Multivariable analyses identified intratumor arteries (odds ratio [OR]: 0.23; 95% confidence interval [CI]: 0.07-0.63; p = 0.007), intratumor hemorrhage (OR: 0.08; 95% CI: 0.01-0.50; p = 0.012), positive HBsAg or HCVAB status (OR: 4.52; 95% CI: 1.65-13.29; p = 0.004), platelet count (≥186.5 × 109 /L, OR: 0.38; 95% CI: 0.16-0.86; p = 0.022), and aspartate transaminase level (≥33.2 IU/l, OR: 0.24; 95% CI: 0.09-0.59; p = 0.003) as independent predictors of intra-tumoral TLSs. AUC of the regression-based model was 0.79 (95% CI:0.72-0.86) and average AUC at 5-fold cross-validation was 0.75 (95% CI: 0.71-0.80). CONCLUSIONS: CT-based nomogram is promising for preoperative prediction of intra-tumoral TLS in HCC.

Spleen Radiomics Signature: A Potential Biomarker for Prediction of Early and Late Recurrences of Hepatocellular Carcinoma After Resection.

OBJECTIVES: To explore the usefulness of spleen radiomics features based on contrast-enhanced computed tomography (CECT) in predicting early and late recurrences of hepatocellular carcinoma (HCC) patients after curative resection. METHODS: This retrospective study included 237 HCC patients who underwent CECT and curative resection between January 2006 to January 2016. Radiomic features were extracted from CECT images, and then the spleen radiomics signatures and the tumor radiomics signatures were built. Cox regression analysis was performed to identify the independent risk factors of early and late recurrences. Then, multiple models were built to predict the recurrence-free survival of HCC after resection, and the incremental value of the radiomics signature to the clinicopathologic model was assessed and validated. Kaplan-Meier survival analysis was used to assess the association of the models with RFS. RESULTS: The spleen radiomics signature was independent risk factor of early recurrence of HCC. The mixed model that integrated microvascular invasion, tumor radiomics signature and spleen radiomics signature for the prediction of early recurrence achieved the highest C-index of 0.780 (95% CI: 0.728,0.831) in the primary cohort and 0.776 (95% CI: 0.716,0.836) in the validation cohort, and presented better predictive performance than clinicopathological model and combined model. In the analysis of late recurrence, the spleen radiomics signature was the only prognostic factor associated with late recurrence of HCC. CONCLUSIONS: The identified spleen radiomics signatures are prognostic factors of both early and late recurrences of HCC patients after surgery and improve the predictive performance of model for early recurrence.

Combining quantitative and qualitative magnetic resonance imaging features to differentiate anorectal malignant melanoma from low rectal cancer.

Background: Distinguishing anorectal malignant melanoma from low rectal cancer remains challenging because of the overlap of clinical symptoms and imaging findings. We aim to investigate whether combining quantitative and qualitative magnetic resonance imaging (MRI) features could differentiate anorectal malignant melanoma from low rectal cancer. Methods: Thirty-seven anorectal malignant melanoma and 98 low rectal cancer patients who underwent pre-operative rectal MRI from three hospitals were retrospectively enrolled. All patients were divided into the primary cohort (N = 84) and validation cohort (N = 51). Quantitative image analysis was performed on T1-weighted (T1WI), T2-weighted (T2WI), and contrast-enhanced T1-weighted imaging (CE-T1WI). The subjective qualitative MRI findings were evaluated by two radiologists in consensus. Multivariable analysis was performed using stepwise logistic regression. The discrimination performance was assessed by the area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). Results: The skewness derived from T2WI (T2WI-skewness) showed the best discrimination performance among the entire quantitative image features for differentiating anorectal malignant melanoma from low rectal cancer (primary cohort: AUC = 0.852, 95% CI 0.788-0.916; validation cohort: 0.730, 0.645-0.815). Multivariable analysis indicated that T2WI-skewness and the signal intensity of T1WI were independent factors, and incorporating both factors achieved good discrimination performance in two cohorts (primary cohort: AUC = 0.913, 95% CI 0.868-0.958; validation cohort: 0.902, 0.844-0.960). Conclusions: Incorporating T2WI-skewness and the signal intensity of T1WI achieved good performance for differentiating anorectal malignant melanoma from low rectal cancer. The quantitative image analysis helps improve diagnostic accuracy.