Hepatobiliary phase signal intensity: A potential method of diagnosing HCC with atypical imaging features among LR-M observations

Herein, we assessed whether hepatobiliary phase (HBP) signal intensity (SI) can be used to differentiate HCC and non-HCC malignancies within LR-M observations. 106 LR-M patients based on LI-RADS v2018 who underwent gadoxetate-disodium magnetic resonance imaging and surgery from January 2009 to December 2018 were included. SI of LR-M observation on HBP was analyzed by two radiologists and categorized into dark, low and iso-to-high groups. Tumor was classified as dark when more than 50% of tumor showed hypointensity compared to spleen, as low when more than 50% of tumor showed hyperintensity compared to spleen but hypointensity compared to liver parenchyma, and as iso-to-high if there was even a focal iso-intensity or hyperintensity compared to liver parenchyma. Analysis of clinicopathological factors and association between imaging and histology was performed. Out of 106 LR-M, 42 (40%) were showed dark, 61 (58%) showed low, and 3 (3%) showed iso-to-high SI in HBP. Three iso-to-high SI LR-M were HCCs (P = 0.060) and their major histologic differentiation was Edmondson grade 1 (P = 0.001). 43 out of 61 (71%) low SI LR-M were iCCA or cHCC-CCA (P = 0.002). Inter-reader agreement of HBP SI classification was excellent, with a kappa coefficient of 0.872. LR-M with iso-to-high SI in HBP is prone to being HCC while LR-M with low SI in HBP is prone to being tumor with fibrous stroma such as iCCA and cHCC-CCA. Classification of LR-M based on HBP SI may be a helpful method of differentiating HCC from non-HCC malignancies.

A study of the correlations between IVIM-DWI parameters and the histologic differentiation of hepatocellular carcinoma

The present study aimed to investigate the value of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) in the preoperative prediction of the histologic differentiation of hepatocellular carcinoma (HCC). Seventy HCC patients were scanned with a 3.0 T magnetic resonance scanner. The values of apparent diffusion coefficient (ADC), slow apparent diffusion coefficient (D), fast apparent diffusion coefficient (D*), and the fraction of the fast apparent diffusion coefficient (f) were measured. Analysis of variance was used to compare the differences in parameters between groups with different degrees of histologic differentiation. p < 0.05 was considered statistically significant. Receiver operating characteristic (ROC) curves were used to analyse the efficacy of IVIM-DWI parameters for predicting the histologic differentiation of HCC. The ADC and D values for well, moderately and poorly differentiated HCC were 1.35 ± 0.17 × 10−3 mm2/s, 1.16 ± 0.17 × 10−3 mm2/s, 0.98 ± 0.21 × 10−3 mm2/s, and 1.06 ± 0.15 × 10−3 mm2/s, 0.88 ± 0.16 × 10−3 mm2/s, 0.76 ± 0.18 × 10−3 mm2/s, respectively, and all differences were significant. The D* and f values of the three groups were 32.87 ± 14.70 × 10−3 mm2/s, 41.68 ± 17.90 × 10−3 mm2/s, 34.54 ± 18.60 × 10−3 mm2/s and 0.22 ± 0.07, 0.23 ± 0.08, 0.18 ± 0.07, respectively, with no significant difference. When the cut-off values of ADC and D were 1.25 × 10−3 mm2/s and 0.97 × 10−3 mm2/s, respectively, their diagnostic sensitivities and specificities for distinguishing well differentiated HCC from moderately differentiated and poorly differentiated HCC were 73.3%, 85.5%, 86.7%, and 78.2%, and their areas under the ROC curve were 0.821 and 0.841, respectively. ADC and D values can be used preoperatively to predict the degree of histologic differentiation in HCC, and the D value has better diagnostic value.

Molecular Signatures and Their Clinical Utility in Pancreatic Neuroendocrine Tumors

Pancreatic neuroendocrine tumors (PNETs) are classified based on their histologic differentiation and proliferative indices, which have been used extensively to determine prognosis. Advances in next-generation sequencing and other high-throughput techniques have allowed researchers to objectively explore tumor specimens and learn about the genetic alterations associated with malignant transformation in PNETs. As a result, targeted, pathway-specific therapies have been emerging for the treatment of unresectable and metastatic disease. As we continue to trial various pharmaceutical products, evidence from studies using multi-omics approaches indicates that clinical aggressiveness stratifies along other genotypic and phenotypic demarcations, as well. In this review, we explore the clinically relevant and potentially targetable molecular signatures of PNETs, their associated trials, and the overall differences in reported prognoses and responses to existing therapies.

Case Report: Multidisciplinary Approach for a Rare Case of Thymic Vascular Malformation

We report the rare case of a 2.5 cm in size mass diagnostic for residual thymus associated with venous vascular malformation (ISSVA classification, 2008) in a 58 years old man. Diagnosis was obtained only after surgical removal that was complicated by a sudden massive bleeding (about 1,500 cc) requiring emergency conversion to median sternotomy. Difficulty in preoperative diagnosis, rarity of histologic pattern, and surgical challenges make this case very interesting for surgeons, pathologists and radiologist. Our message, dealing with mediastinal masses, is: (a) differential diagnosis between the more frequent solid antero-superior mediastinal tumors and vascular malformation should be always considered (b) preoperative angiography should always be performed in case of uncertain diagnosis (c) coil embolization should always be considered to reduce potentially fatal bleeding (d) histologic differentiation with other thymic neoplasms must be always considered.

Medical Treatment of Advanced Pancreatic Neuroendocrine Neoplasms

Pancreatic neuroendocrine neoplasms (panNENs) are relatively rare but their incidence has increased almost sevenfold over the last four decades. Neuroendocrine neoplasms are classified according to their histologic differentiation and their grade. Their grade is based on their Ki-67 proliferation index and mitotic index. Their prognosis is highly variable according to these elements and treatments also vary according to their classification. Surgery is the only curative treatment for localized and advanced panNENs and offers a better prognosis than non-surgical treatments. In the case of an advanced panNEN without the possibility of resection and/or ablation, medical treatment remains the cornerstone for improving survival and preserving quality-of-life. PanNENs are considered as chemosensitive tumors, unlike midgut neuroendocrine tumors. Thus, panNENs can be treated with chemotherapy, but targeted therapies and somatostatin analogs are also treatment options. The scarcity and heterogeneity of NENs make their management difficult. The present review aims to clarify the medical treatments currently available for advanced panNENs, based on their characteristics, and to propose a treatment algorithm.

Colorectal Signet Ring Cell Carcinoma in a Young Pregnant Woman

Colorectal cancer during pregnancy is one of the less common neoplasms with an incidence of 0.8 in 100,000 pregnancies. Primary colonic signet ring cell carcinoma is a weird variety, characterized by a poor histologic differentiation, with a high morbidity-mortality rate. The case of a 24-year-old patient with a 22-week-old pregnancy and colorectal cancer stage IV in palliative state is presented, with a devastating result. Early diagnosis represents a challenge because of the presentation form and the histologic aggressiveness of this disease. We suggest that colorectal cancer during pregnancy must be treated by a multidisciplinary team.

A Lesion-Based Convolutional Neural Network Improves Endoscopic Detection and Depth Prediction of Early Gastric Cancer

In early gastric cancer (EGC), tumor invasion depth is an important factor for determining the treatment method. However, as endoscopic ultrasonography has limitations when measuring the exact depth in a clinical setting as endoscopists often depend on gross findings and personal experience. The present study aimed to develop a model optimized for EGC detection and depth prediction, and we investigated factors affecting artificial intelligence (AI) diagnosis. We employed a visual geometry group(VGG)-16 model for the classification of endoscopic images as EGC (T1a or T1b) or non-EGC. To induce the model to activate EGC regions during training, we proposed a novel loss function that simultaneously measured classification and localization errors. We experimented with 11,539 endoscopic images (896 T1a-EGC, 809 T1b-EGC, and 9834 non-EGC). The areas under the curves of receiver operating characteristic curves for EGC detection and depth prediction were 0.981 and 0.851, respectively. Among the factors affecting AI prediction of tumor depth, only histologic differentiation was significantly associated, where undifferentiated-type histology exhibited a lower AI accuracy. Thus, the lesion-based model is an appropriate training method for AI in EGC. However, further improvements and validation are required, especially for undifferentiated-type histology.