Soft tissue aneurysmal bone cyst presenting as an enlarging neck mass: Case report and review of the head and neck literature.

Soft tissue aneurysmal bone cysts (STABCs) are rare neoplasms histopathologically identical to aneurysmal bone cysts. These benign lesions are characterized by thin, peripheral ossification and no skeletal continuity. STABC may be difficult to distinguish from myositis ossificans (MO) and malignant entities from imaging and fine needle aspiration, due to rarity and overlapping features. We present a case of a STABC occurring in the paraspinal cervical muscles. The imaging, histopathology, molecular analysis, and treatment are discussed. Four other published cases of STABC of the head and neck are reviewed.

Analysis and Validation of Image Search Engines in Histopathology.

Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient tissue comparison for various purposes, ranging from triaging and diagnosis to prognosis and prediction. Whole slide images (WSIs) are highly detailed digital representations of tissue specimens mounted on glass slides. Matching WSI to WSI can serve as the critical method for patient tissue comparison. In this paper, we report extensive analysis and validation of four search methods bag of visual words (BoVW), Yottixel, SISH, RetCCL, and some of their potential variants. We analyze their algorithms and structures and assess their performance. For this evaluation, we utilized four internal datasets (1269 patients) and three public datasets (1207 patients), totaling more than 200, 000 patches from 38 different classes/subtypes across five primary sites. Certain search engines, for example, BoVW, exhibit notable efficiency and speed but suffer from low accuracy. Conversely, search engines like Yottixel demonstrate efficiency and speed, providing moderately accurate results. Recent proposals, including SISH, display inefficiency and yield inconsistent outcomes, while alternatives like RetCCL prove inadequate in both accuracy and efficiency. Further research is imperative to address the dual aspects of accuracy and minimal storage requirements in histopathological image search.

High-risk human papilloma virus status & outcomes for penile squamous cell carcinoma: A single institution experience.

OBJECTIVES: There is a paucity of data on North American cohorts of patients with penile squamous cell carcinoma (pSCC). Herein, we aimed to assess the sensitivity of various modalities to identify human papillomavirus (HPV) status, determine the prevalence of high-risk HPV-positivity, and evaluate the prognostic impact of relevant clinicopathologic variables. METHODS: Patients with pSCC (n = 121) consecutively treated with partial/total penectomy (2000-2022) at a single institution were included. HPV status (based on immunohistochemistry [IHC], in situ hybridization [ISH], and panviral metagenomic sequencing [PMS]), histologic features, and outcomes were reviewed. Outcome events included death due to disease and progression. RESULTS: The majority of patients were white (105/121, 86.8%). Thirty-seven (30.6%) were high-risk HPV-positive, and morphologic evaluation had a sensitivity of 97.3% (95% confidence interval [CI], 86.2-99.5) for predicting high-risk HPV status compared to IHC/ISH/PMS. Disease progression was more common among high-risk HPV-negative compared to high-risk HPV-positive patients (HR 2.74, CI 1.12-8.23, P = 0.03). Moreover, among high-risk HPV-negative patients, those with moderate-poorly differentiated tumors had increased disease-specific mortality (32.6%, CI 17.1-48.1) compared to those with well-differentiated tumors (0%). Among high-risk HPV-positive patients, those with basaloid morphology had lower disease-specific mortality (0% vs 14.4%, CI 0.0-33.1). CONCLUSIONS: We demonstrate high-risk HPV-positivity in approximately one-third of patients with pSCC. Morphologic evaluation alone had a high sensitivity in correctly determining HPV status. Our results suggest that high-risk HPV status and morphologic features (differentiation in high-risk HPV-negative, and basaloid subtype in high-risk HPV-positive pSCC) may have prognostic value.

Comparison of a modified staging system with 8th edition AJCC criteria in a North American cohort of pT2/pT3 HPV-negative penile squamous cell carcinoma.

The staging for pT2/pT3 penile squamous cell carcinoma (pSCC) has undergone major changes. Some authors proposed criteria wherein the distinction between pT2/pT3 was made using the same histopathological variables that are currently utilized to differentiate pT1a/pT1b. In this single-institution, North American study, we focused on (HPV-negative) pT2/3 pSCCs (i.e., tumors invading corpus spongiosum/corpus cavernosum), and compared the prognostic ability of the following systems: (i) AJCC (8th edition) criteria; (ii) modified staging criteria proposed by Sali et al. (Am J Surg Pathol. 2020; 44:1112-7). In the proposed system, pT2 tumors were defined as those devoid of lymphovascular invasion (LVI) or perineural invasion (PNI), and were not poorly differentiated; whereas pT3 showed one or more of the following: LVI, PNI, and/or grade 3. 48 pT2/pT3 cases were included (AJCC, pT2: 27 and pT3: 21; Proposed, pT2: 22 and pT3: 26). The disease-free survival (DFS) and progression-free survival (PFS) did not differ between pT2 and pT3, following the current AJCC definitions (p = 0.19 and p = 0.10, respectively). When the pT2/3 stages were reconstructed using the modified criteria, however, a statistically significant difference was present in both DFS and PFS between pT2 and pT3 (p = 0.004 and p = 0.003, respectively). The proposed staging system has the potential to improve the prognostication of pT2/pT3 tumors in pSCC. Each of these histopathologic variables has been shown to have a significant association with outcomes in pSCC, which is an advantage. Further studies are needed to demonstrate the utility of this modified staging system in patient populations from other geographic regions.

Democratizing Artificial Intelligence in Anatomic Pathology.

CONTEXT.­: Artificial intelligence is a transforming technology for anatomic pathology. Involvement within the workforce will foster support for algorithm development and implementation. OBJECTIVE.­: To develop a supportive ecosystem that enables pathologists with variable expertise in artificial intelligence to create algorithms in a development environment with seamless transition to a production environment. RESULTS.­: The development team considered internal development and vended solutions. Because of the extended timeline and resource requirements for internal development, a decision was made to use a vended solution. Vendor proposals were solicited and reviewed by pathologists, IT, and security groups. A vendor was selected and pipelines for development and production were established. Proposals for development were solicited from the pathology department. Eighty-four investigators were selected for the initial cohort, receiving training and access to dedicated subject matter experts. A total of 30 of 31 projects progressed through the model development process of annotating, training, and validation. Based on these projects, 15 abstracts were submitted to national meetings. CONCLUSIONS.­: Democratizing artificial intelligence by creating an ecosystem to support pathologists with varying levels of expertise can break down entry barriers, reduce overall cost of algorithm development, improve algorithm quality, and enhance the speed of adoption.

Creating an atlas of normal tissue for pruning WSI patching through anomaly detection.

Patching whole slide images (WSIs) is an important task in computational pathology. While most of them are designed to classify or detect the presence of pathological lesions in a WSI, the confounding role and redundant nature of normal histology are generally overlooked. In this paper, we propose and validate the concept of an "atlas of normal tissue" solely using samples of WSIs obtained from normal biopsies. Such atlases can be employed to eliminate normal fragments of tissue samples and hence increase the representativeness of the remaining patches. We tested our proposed method by establishing a normal atlas using 107 normal skin WSIs and demonstrated how established search engines like Yottixel can be improved. We used 553 WSIs of cutaneous squamous cell carcinoma to demonstrate the advantage. We also validated our method applied to an external dataset of 451 breast WSIs. The number of selected WSI patches was reduced by 30% to 50% after utilizing the proposed normal atlas while maintaining the same indexing and search performance in leave-one-patient-out validation for both datasets. We show that the proposed concept of establishing and using a normal atlas shows promise for unsupervised selection of the most representative patches of the abnormal WSI patches.