Background: The role of Artificial intelligence (AI) which is defined as the ability of computers to perform tasks that normally require human intelligence is constantly expanding. Medicine was slow to embrace AI. However, the role of AI in medicine is rapidly expanding and promises to revolutionize patient care in the coming years. In addition, it has the ability to democratize high level medical care and make it accessible to all parts of the world. Main text: Among specialties of medicine, some like radiology were relatively quick to adopt AI whereas others especially pathology (and surgical pathology in particular) are only just beginning to utilize AI. AI promises to play a major role in accurate diagnosis, prognosis and treatment of cancers. In this paper, the general principles of AI are defined first followed by a detailed discussion of its current role in medicine. In the second half of this comprehensive review, the current and future role of AI in surgical pathology is discussed in detail including an account of the practical difficulties involved and the fear of pathologists of being replaced by computer algorithms. A number of recent studies which demonstrate the usefulness of AI in the practice of surgical pathology are highlighted. Conclusion: AI has the potential to transform the practice of surgical pathology by ensuring rapid and accurate results and enabling pathologists to focus on higher level diagnostic and consultative tasks such as integrating molecular, morphologic and clinical information to make accurate diagnosis in difficult cases, determine prognosis objectively and in this way contribute to personalized care.

Background: Solitary Fibrous Tumor (SFT) is a distinct soft tissue neoplasm associated with NAB2-STAT6 gene fusion. It can involve a number of anatomic sites and exhibits a wide spectrum of histological features. Main body: Apart from diversity in morphological features seen even in conventional SFT, two histologic variants (fat-forming and giant cell-rich) are also recognized. In addition, a malignant form and dedifferentiation are well recognized. Owing to diverse histological features and involvement of diverse anatomic locations, SFT can mimic other soft tissue neoplasms of different lineages including schwannoma, spindle cell lipoma, dermatofibrosarcoma protuberans, liposarcoma, gastrointestinal stromal tumor (GIST), malignant peripheral nerve sheath tumor (MPNST), and synovial sarcoma. SFT is classified as an intermediate (rarely metastasizing) tumor according to World Health Organization Classification of Tumors of Soft tissue and Bone, 5th edition. The management and prognosis of SFT differs from its malignant mimics and correct diagnosis is therefore important. Although SFT expresses a distinct immunohistochemical (IHC) profile, the classic histomorphological and IHC profile is not seen in all cases and diagnosis can be challenging. NAB2-STAT6 gene fusion has recently emerged as a sensitive and specific molecular marker and its IHC surrogate marker signal transducer and activator of transcription 6 (STAT6) has also shown significant sensitivity and specificity. However, few recent studies have reported STAT6 expression in other soft tissue neoplasms. Conclusion: This review will focus on describing the diversity of histological features of SFT, differential diagnoses and discussing the features helpful in distinguishing SFT from its histological mimics.

Pakistan’s population is ethnically diverse with distinct ethnic groups inhabiting various parts of the country. Cancer statistics obtained from specific regions populated by distinct ethnic groups may vary considerably. There is no national cancer registry. To determine whether there are indeed significant statistical differences in cancer incidence and prevalence, data was recorded from different parts of Pakistan based on the ethnic composition of the population in those parts. Ten papers (original articles) on cancer incidence and prevalence in Pakistan published in the last two decades were selected from PubMed and Google Scholar. Meta-analysis of findings of these studies was performed using Meta-analysis of Observational Studies in Epidemiology (MOOSE) checklist. χ2-based I2 test was used for evaluating heterogeneity and Forest plots were generated for calculating unadjusted prevalence estimates. Oral, gastric, prostate, breast, and colorectal cancers were selected for meta-analysis. I2 values of 75% or greater indicated high heterogeneity. All five types of cancer selected for meta-analysis (performed on studies carrying similar statistical weights) showed extremely high heterogeneity with I2 values of 99.7% for oral cancer, 98.6% for prostate cancer, 98.3% for gastric cancer, 99.8% for breast cancer, and 85.4% for colorectal cancer. p values for all cancers were highly statistically significant. Our findings show that the prevalence rates of different cancer types demonstrate marked variation in different studies depending on the place of origin of the study and dominant ethnic group in that region, and these variations are highly statistically significant. A national cancer registry needs to be established as soon as possible.

State-of-the-art artificial intelligence (AI) has recently gained considerable interest in the healthcare sector and has provided solutions to problems through automated diagnosis. Cytological examination is a crucial step in the initial diagnosis of cancer, although it shows limited diagnostic efficacy. Recently, AI applications in the processing of cytopathological images have shown promising results despite the elementary level of the technology. Here, we performed a systematic review with a quantitative analysis of recent AI applications in non-gynecological (non-GYN) cancer cytology to understand the current technical status. We searched the major online databases, including MEDLINE, Cochrane Library, and EMBASE, for relevant English articles published from January 2010 to January 2021. The searched query terms were: “artificial intelligence”, “image processing”, “deep learning”, “cytopathology”, and “fine-needle aspiration cytology.” Out of 17,000 studies, only 26 studies (26 models) were included in the full-text review, whereas 13 studies were included for quantitative analysis. There were eight classes of AI models treated of according to target organs: thyroid (n = 11, 39%), urinary bladder (n = 6, 21%), lung (n = 4, 14%), breast (n = 2, 7%), pleural effusion (n = 2, 7%), ovary (n = 1, 4%), pancreas (n = 1, 4%), and prostate (n = 1, 4). Most of the studies focused on classification and segmentation tasks. Although most of the studies showed impressive results, the sizes of the training and validation datasets were limited. Overall, AI is also promising for non-GYN cancer cytopathology analysis, such as pathology or gynecological cytology. However, the lack of well-annotated, large-scale datasets with Z-stacking and external cross-validation was the major limitation found across all studies. Future studies with larger datasets with high-quality annotations and external validation are required.

Lipoblastoma is a rare neoplasm that occurs mostly in infants and children. Although benign, it has a tendency for local recurrence. Clinical and pathological features of 23 cases of lipoblastoma described. Patients’ age ranged from 8 months to 18 years with mean and median age 4.1 and 2.5 years, respectively. Male:female ratio was 2.8:1. Most common sites were lower extremities (9 cases), followed by abdominal cavity and retroperitoneum (4 cases), and scrotum/groin (3 cases). Grossly, 22 tumors were well circumscribed and multi nodular. All cases showed lobules composed of adipocytes and lipoblasts with intervening fibrous septa and fine vascular network. Myxoid change, capsule formation and septation were seen in all cases. Zonation was seen in 2 cases. Follow-up was available in 14 out of 23 patients. Of these, 13 were alive and free of disease with no evidence of any recurrent lesion. One patient with a mediastinal infiltrating lipoblastoma experienced 4 recurrences. Lipoblastoma is a benign adipocytic neoplasm of infants and young children. Correlation of clinical and histological features helps in reaching a correct diagnosis. Owing to a high recurrence rate following incomplete resection, a complete resection is essential. Prognosis is excellent after complete resection.

The integration of digital pathology (DP) with artificial intelligence (AI) enables faster, more accurate, and thorough diagnoses, leading to more precise personalized treatment. As technology is advancing rapidly, it is critical to understand the current state of AI applications in DP. Therefore, a patent analysis of AI in DP is required to assess the application and publication trends, major assignees, and leaders in the field. We searched five major patent databases, namely, those of the USPTO, EPO, KIPO, JPO, and CNIPA, from 1974 to 2021, using keywords such as DP, AI, machine learning, and deep learning. We discovered 6284 patents, 523 of which were used for trend analyses on time series, international distribution, top assignees; word cloud analysis; and subject category analyses. Patent filing and publication have increased exponentially over the past five years. The United States has published the most patents, followed by China and South Korea (248, 117, and 48, respectively). The top assignees were Paige.AI, Inc. (New York City, NY, USA) and Siemens, Inc. (Munich, Germany) The primary areas were whole-slide imaging, segmentation, classification, and detection. Based on these findings, we expect a surge in DP and AI patent applications focusing on the digitalization of pathological images and AI technologies that support the vital role of pathologists.