Pancreatic cancer (PDAC) is one of the most aggressive solid tumors and is showing increasing incidence. The aim of our review is to provide practical help for all clinical oncologists and to summarize the current management of PDAC using a simple “ABC method” (A—anatomical resectability, B—biological resectability and C—clinical conditions). For anatomically resectable PDAC without any high-risk factors (biological or conditional), the actual standard of care is represented by surgery followed by adjuvant chemotherapy. The remaining PDAC patients should all be treated with initial systemic therapy, though the intent for each is different: for borderline resectable patients, the intent is neoadjuvant; for locally advanced patients, the intent is conversion; and for metastatic PDAC patients, the intent remains just palliative. The actual standard of care in first-line therapy is represented by two regimens: FOLFIRINOX and gemcitabine/nab-paclitaxel. Recently, NALIRIFOX showed positive results over gemcitabine/nab-paclitaxel. There are limited data for maintenance therapy after first-line treatment, though 5-FU or FOLFIRI after initial FOLFIRINOX, and gemcitabine, after initial gemcitabine/nab-paclitaxel, might be considered. We also dedicate space to special rare conditions, such as PDAC with germline BRCA mutations, pancreatic acinar cell carcinoma and adenosquamous carcinoma of the pancreas, with few clinically relevant remarks.

A Simple Overview of Pancreatic Cancer Treatment for Clinical Oncologists

This study was performed to explore the effect of childhood trauma on nonsuicidal self‐injury (NSSI) in adolescents with bipolar II (BD II) depression.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/brb3.2771

Effects of childhood trauma on nonsuicidal self‐injury in adolescent patients with bipolar II depression

Dual energy X-ray absorptiometry (DXA) is widely used modality for measuring bone mineral density (BMD). DXA is used to measure the quantitative areal BMD of bone, but has the disadvantage of not reflecting the bone architecture. To compensate for this disadvantage, trabecular bone score (TBS), a qualitative parameter of trabecular microarchitecture, is used. Meanwhile, there have been recent attempts to diagnose osteoporosis using the Hounsfield unit (HU) from CT and MR-based proton density fat fraction (PDFF) measurements. In our study, we aimed to find out the correlation between HU/PDFF and BMD/TBS, and whether osteoporosis can be diagnosed through HU/PDFF. Our study revealed that the HU value showed a moderate to good positive correlation with BMD and TBS. PDFF showed a fair negative correlation with BMD and TBS. In diagnosing osteopenia and osteoporosis, the HU value showed good performance, whereas the PDFF showed fair performance. In conclusion, both HU values and PDFF can play a role in predicting BMD and TBS. Both HU values and PDFF can be used to predict osteoporosis; further, CT is expected to show better results. 

CT and MR for bone mineral density and trabecular bone score assessment in osteoporosis evaluation

MRI-based vertebral bone quality score: relationship with age and reproducibility

To compare the imaging parameters pre- and post- reductive procedure for atlantoaxial dislocation via posterior fixation using pedicle screw and rod. Thirty-seven patients suffering from atlantoaxial dislocation underwent posterior reduction and internal fixation by pedicle screw and rod. We measured pre-operative and post-operative atlantodental interval (ADI), clivus-canal angle (CCA), cervicomedullary angle (CMA), sum of lateral mass interspace (SLMI) of the operation and the control group. ADI, CCA, CMA, and SLMI between the pre-operative and post-operative conditions of the operation group and the control group were compared. The ADI, CCA, CMA, and SLMI in the pre-operative condition of the operation group were 8.3 ± 4.3 mm, 130.2 ± 14.2°, 133.8 ± 16.7°, and 3.7 ± 1.3 mm, respectively, those in the post-operative condition of the operation group were 1.0 ± 0.9 mm, 148.5 ± 9.4°, 156.0 ± 8.2°, and 8.0 ± 2.7 mm, respectively, while those in the control group were 1.2 ± 0.3 mm, 152.7 ± 5.3°, 160.2 ± 6.3°, and 4.5 ± 1.0 mm respectively. Post-operative ADI, CCA, CMA, and SLMI were statistically different (p < 0.01) from pre-operative assessments. The SLMI has no significant difference between the pre-operative condition and the control group. Post-operative SLMI was statistically different from that of the control group. The lateral mass joints were widened after the anatomical reduction of atlantoaxial dislocation by pedicle screw and rod. Widening of the lateral mass exists in both atlantoaxial fusion and occipital-cervical fusion.

/pdf/comparison-of-imaging-parameters-pre-and-post-reductive-bnbjcnd95d.pdf

Comparison of imaging parameters pre- and post- reductive procedure for atlantoaxial dislocation via posterior fixation using pedicle screw and rod: a cross-sectional study.

Although the probability of pancreatic cystic neoplasms (PCNs) being detected is raising year by year, their differential diagnosis and individualized treatment are still a challenge in clinical work. PCNs are tumors containing cystic components with different biological behaviors, and their clinical manifestations, epidemiology, imaging features, and malignant risks are different. Some are benign [e.g., serous cystic neoplasms (SCNs)], with a barely possible that turning into malignant, while others display a low or higher malignant risk [e.g., solid pseudopapillary neoplasms (SPNs), intraductal papillary mucinous neoplasms (IPMNs), and mucinous cystic neoplasms (MCNs)]. PCN management should concentrate on preventing the progression of malignant tumors while preventing complications caused by unnecessary surgical intervention. Clinically, various advanced imaging equipment are usually combined to obtain a more reliable preoperative diagnosis. The challenge for clinicians and radiologists is how to accurately diagnose PCNs before surgery so that corresponding surgical methods and follow-up strategies can be developed or not, as appropriate. The objective of this review is to sum up the clinical features, imaging findings and management of the most common PCNs according to the classic literature and latest guidelines.

/pdf/cystic-neoplasms-of-the-pancreas-differential-diagnosis-and-jqsswlxn.pdf

Cystic Neoplasms of the Pancreas: Differential Diagnosis and Radiology Correlation

Posterior C1-C2 screw-rod fixation and autograft fusion for the treatment of os odontoideum with C1-C2 instability.

Automatic segmentation of liver tumors is crucial to assist radiologists in clinical diagnosis. While various deep learningbased algorithms have been proposed, such as U-Net and its variants, the inability to explicitly model long-range dependencies in CNN limits the extraction of complex tumor features. Some researchers have applied Transformer-based 3D networks to analyze medical images. However, the previous methods focus on modeling the local information (eg. edge) or global information (eg. morphology) with fixed network weights. To learn and extract complex tumor features of varied tumor size, location, and morphology for more accurate segmentation, we propose a Dynamic Hierarchical Transformer Network, named DHT-Net. The DHT-Net mainly contains a Dynamic Hierarchical Transformer (DHTrans) structure and an Edge Aggregation Block (EAB). The DHTrans first automatically senses the tumor location by Dynamic Adaptive Convolution, which employs hierarchical operations with the different receptive field sizes to learn the features of various tumors, thus enhancing the semantic representation ability of tumor features. Then, to adequately capture the irregular morphological features in the tumor region, DHTrans aggregates global and local texture information in a complementary manner. In addition, we introduce the EAB to extract detailed edge features in the shallow fine-grained details of the network, which provides sharp boundaries of liver and tumor regions. We evaluate DHT-Net on two challenging public datasets, LiTS and 3DIRCADb. The proposed method has shown superior liver and tumor segmentation performance compared to several state-of-the-art 2D, 3D, and 2.5D hybrid models.

DHT-Net: Dynamic Hierarchical Transformer Network for Liver and Tumor Segmentation

Preoperative pathological grading assessment is important for patients with breast phyllodes tumors (PTs).

Pretreatment Multiparametric MRI‐Based Radiomics Analysis for the Diagnosis of Breast Phyllodes Tumors

The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT and FA remains a great challenge for radiologists in clinical work. Artificial intelligence (AI)-assisted diagnosis has shown promise in distinguishing PT from FA. However, a very small sample size was adopted in previous studies. In this work, we retrospectively enrolled 656 breast tumors (372 FAs and 284 PTs) with 1945 ultrasound images in total. Two experienced ultrasound physicians independently evaluated the ultrasound images. Meanwhile, three deep-learning models (i.e., ResNet, VGG, and GoogLeNet) were applied to classify FAs and PTs. The robustness of the models was evaluated by fivefold cross validation. The performance of each model was assessed by using the receiver operating characteristic (ROC) curve. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. Among the three models, the ResNet model yielded the highest AUC value, of 0.91, with an accuracy value of 95.3%, a sensitivity value of 96.2%, and a specificity value of 94.7% in the testing data set. In contrast, the two physicians yielded an average AUC value of 0.69, an accuracy value of 70.7%, a sensitivity value of 54.4%, and a specificity value of 53.2%. Our findings indicate that the diagnostic performance of deep learning is better than that of physicians in the distinction of PTs from FAs. This further suggests that AI is a valuable tool for aiding clinical diagnosis, thereby advancing precision therapy.

/pdf/differentiation-between-phyllodes-tumors-and-fibroadenomas-2es3r8xu.pdf

Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians

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