Double Negativity of MRI-Detected and Pathologically-Diagnosed Extramural Venous Invasion is a Favorable Prognostic Factor for Rectal Cancer.

BACKGROUND: Extramural venous invasion (EMVI) is a prognostic factor in rectal cancer. There are two types: EMVI detected by magnetic resonance imaging (MRI) (mr-EMVI) and EMVI detected by pathology (p-EMVI). They have been separately evaluated, but they have not yet been concurrently evaluated. We therefore evaluate both mr-EMVI and p-EMVI in rectal cancer at the same time and clarify their association with prognosis. PATIENTS AND METHODS: Included were the 186 consecutive patients who underwent complete radical resection of tumors ≤ stage III at Wakayama Medical University Hospital, Japan, between 2010 and 2018. All underwent preoperative MRI examination, and were reassessed for EMVI by a radiologist. Surgically resected specimens were then reassessed for EMVI by a pathologist. We assessed the correlation between positivity of mr-EMVI and p-EMVI and prognosis, and the clinicopathological background behind them. RESULTS: Patients with double negativity for mr-EMVI and p-EMVI had better prognosis than patients with mr-EMVI or p-EMVI positivity (p < 0.0001). Positivity for mr-EMVI or p-EMVI was a poor independent prognostic factor in multivariate analysis. CONCLUSIONS: Combined analysis of mr-EMVI and p-EMVI may enable prediction of postoperative prognosis of rectal cancer. Patients with double negativity of mr-EMVI and p-EMVI had better prognosis than patients with some form of positivity. Stated differently, patients with positivity of mr-EMVI, p-EMVI, or both had a poorer prognosis than those with double negativity. Postoperative adjuvant chemotherapy may improve poor prognosis. Combined evaluation of mr-EMVI and p-EMVI may be used to predict clinical outcomes and may be an effective prognostic predictor of rectal cancer.

Implication of KMT2C and TSC2 variants in the tumorigenesis of acquired cystic disease-associated renal cell carcinomas.

In 2020, acquired cystic disease-associated renal cell carcinomas (ACD-RCCs) were reported to harbor KMT2C and TSC2 variants: however, their carcinogenic implication has not yet been reported. This study aimed to explore the variant features of KMT2C and TSC2 in ACD-RCC and their implication in ACD-RCC tumorigenesis. Eleven ACD-RCCs, 10 ACD-RCC-like cysts, and 18 background kidneys were retrieved. The background kidneys consisted of atrophic thyroid follicle-like tubules. They included four with clustered cysts, two with eosinophilic changes, and one each with clear cell changes and sieve-like changes in the renal tubules. First, DNA-targeted sequencing of KMT2C and TSC2 whole exons was performed on eight ACD-RCC samples. Subsequently, a custom DNA panel was designed to include the recurrent KMT2C and TSC2 variants based on the sequencing results. Second, DNA-targeted sequencing was performed on the remaining samples using a custom panel targeting the recurrent variants. Additionally, immunohistochemistry was performed for KMTC, H3K4me1, H3K4me3, TSC2, and GPNMB on the ACD-RCCs. Six of the 11 ACD-RCC cases harbored KMT2C and TSC2 variants, including nine likely pathogenic variants. In contrast to ACD-RCC, 1 of the 9 ACD-RCC-like cysts harbored both variants. Immunohistochemical analysis did not support the loss of function in ACD-RCCs harboring KMT2C and TSC2 variants. KMT2C and TSC2 variant frequencies were higher in ACD-RCC than in other renal cell carcinomas. However, KMT2C and TSC2 are unlikely to be the primary drivers of ACD-RCC development.

Artificial Intelligence Detected the Relationship Between Nuclear Morphological Features and Molecular Abnormalities of Papillary Thyroid Carcinoma.

Papillary thyroid carcinoma (PTC) is the most common type of thyroid carcinoma and has characteristic nuclear features. Genetic abnormalities of PTC affect recent molecular target therapeutic strategy towards RET-altered cases, and they affect clinical prognosis and progression. However, there has been insufficient objective analysis of the correlation between genetic abnormalities and nuclear features. Using our newly developed methods, we studied the correlation between nuclear morphology and molecular abnormalities of PTC with the aim of predicting genetic abnormalities of PTC. We studied 72 cases of PTC and performed genetic analysis to detect BRAF p.V600E mutation and RET fusions. Nuclear features of PTC, such as nuclear grooves, pseudo-nuclear inclusions, and glassy nuclei, were also automatically detected by deep learning models. After analyzing the correlation between genetic abnormalities and nuclear features of PTC, logistic regression models could be used to predict gene abnormalities. Nuclear features were accurately detected with over 0.90 of AUCs in every class. The ratio of glassy nuclei to nuclear groove and the ratio of pseudo-nuclear inclusion to glassy nuclei were significantly higher in cases that were positive for RET fusions (p = 0.027, p = 0.043, respectively) than in cases that were negative for RET fusions. RET fusions were significantly predicted by glassy nuclei/nuclear grooves, pseudo-nuclear inclusions/glassy nuclei, and age (p = 0.023). Our deep learning models could accurately detect nuclear features. Genetic abnormalities had a correlation with nuclear features of PTC. Furthermore, our artificial intelligence model could significantly predict RET fusions of classic PTC.

Small sized centroblasts as poor prognostic factor in follicular lymphoma - Based on artificial intelligence analysis.

Histological assessment of centroblasts is an important evaluation in the diagnosis of follicular lymphoma, but there is substantial observer variation in assessment among hematopathologists. We aimed to perform quantitative morphological analysis of centroblasts in follicular lymphoma using new artificial intelligence technology in relation to the clinical prognosis. Hematoxylin and eosin slides of lesions were prepared from 36 cases of follicular lymphoma before initial chemotherapy. Cases were classified into three groups by clinical course after initial treatment. The 'excellent prognosis' group were without recurrence or progression of follicular lymphoma within 60 months, the 'poor prognosis' group were those that had relapse, exacerbation, or who died due to the follicular lymphoma within 60 months, and the 'indeterminate prognosis' group were those without recurrence or progression but before the passage of 60 months. We created whole slide images and image patches of hematoxylin and eosin sections for all cases. We designed an object detection model specialized for centroblasts by fine-tuning YOLOv5 and segmented all centroblasts in whole slide images. The morphological characteristics of centroblasts in relation to the clinical prognosis of follicular lymphoma were analyzed. Centroblasts in follicular lymphoma of the poor prognosis group were significantly smaller in nuclear size than those in follicular lymphoma of the excellent prognosis group in the following points: median of nuclear area (p = 0.013), long length (p = 0.042), short length (p = 0.007), nuclear area of top 10 % cells (p = 0.024) and short length of top 10 % cells (p = 0.020). Cases with a mean nuclear area of <55 µm2 had poorer event-free survival than those with a mean nuclear area of ≥55 µm2 (p < 0.0123). AI methodology is suggested to be able to surpass pathologist's observation in capturing morphological features. Small-sized centroblasts will likely become a new prognostic factor of follicular lymphoma.