Utility of artificial intelligence in a binary classification of soft tissue tumors

Soft tissue tumors (STTs) pose diagnostic and therapeutic challenges due to their rarity, complexity, and morphological overlap. Accurate differentiation between benign and malignant STTs is important to set treatment directions, however, this task can be difficult. The integration of machine learning and artificial intelligence (AI) models can potentially be helpful in classifying these tumors. The aim of this study was to investigate AI and machine learning tools in the classification of STT into benign and malignant categories. This study consisted of three components: (1) Evaluation of whole-slide images (WSIs) to classify STT into benign and malignant entities. Five specialized soft tissue pathologists from different medical centers independently reviewed 100 WSIs, representing 100 different cases, with limited clinical information and no additional workup. The results showed an overall concordance rate of 70.4% compared to the reference diagnosis. (2) Identification of cell-specific parameters that can distinguish benign and malignant STT. Using an image analysis software (QuPath) and a cohort of 95 cases, several cell-specific parameters were found to be statistically significant, most notably cell count, nucleus/cell area ratio, nucleus hematoxylin density mean, and cell max caliper. (3) Evaluation of machine learning library (Scikit-learn) in differentiating benign and malignant STTs. A total of 195 STT cases (156 cases in the training group and 39 cases in the validation group) achieved approximately 70% sensitivity and specificity, and an AUC of 0.68. Our limited study suggests that the use of WSI and AI in soft tissue pathology has the potential to enhance diagnostic accuracy and identify parameters that can differentiate between benign and malignant STTs. We envision the integration of AI as a supportive tool to augment the pathologists' diagnostic capabilities.

Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To determine whether time-dependent deep learning models can outperform single timepoint models in predicting preoperative upgrade of ductal carcinoma in situ (DCIS) to invasive malignancy on dynamic contrastenhanced (DCE) breast MRI without lesion segmentation prerequisite. Materials and Methods In this exploratory study, 154 cases of biopsy-proven DCIS (25 upgraded at surgery and 129 not upgraded) were selected consecutively from a retrospective cohort of preoperative DCE MRI in women with an average age of 58.6 years at time of diagnosis from 2012 to 2022. Binary classification was implemented with convolutional neural network-long short-term memory (CNN-LSTM) architectures benchmarked against traditional CNNs without manual segmentation of the lesions. Combinatorial performance analysis of ResNet50 versus VGG16-based models was performed with each contrast phase. Binary classification area under the receiver operating characteristic curve (AUC) was reported. Results VGG16-based models consistently provided better hold-out test AUCs than ResNet50 in CNN and CNNLSTM studies (multiphase test AUC: 0.67 versus 0.59, respectively, for CNN models; P = .04 and 0.73 versus 0.62 for CNN-LSTM models; P = .008). The time-dependent model (CNN-LSTM) provided a better multiphase test AUC over single-timepoint (CNN) models (0.73 versus 0.67, P = .04). Conclusion Compared with single-timepoint architectures, sequential deep learning algorithms using preoperative DCE MRI improved prediction of DCIS lesions upgraded to invasive malignancy without the need for lesion segmentation. ©RSNA, 2024.

Non-Islet Cell Tumor Hypoglycemia Secondary to a 20 cm Intra-Abdominal Leiomyoma in a Male Patient: A Case Report and Literature Review

Non-islet cell tumor hypoglycemia (NICTH) is a rare clinical entity associated with large mesenchymal tumors. Its pathogenesis is most commonly mediated by tumor overproduction of "big" insulin-like growth factor-2. Here, we present a 54-year-old male who presented with noninsulin-mediated hypoglycemia and a 20 cm intra-abdominal leiomyoma. His hypoglycemic episodes resolved after the resection of his tumor. To our knowledge, this is the only documented case in the English literature of NICTH associated with leiomyoma in a male patient. NICTH due to a benign leiomyoma should be in the differential diagnosis for any patient with hypoglycemia and an abdominal mass.

The current state of digital cytology and artificial intelligence (AI): global survey results from the American Society of Cytopathology Digital Cytology Task Force

INTRODUCTION

The integration of whole slide imaging (WSI) and artificial intelligence (AI) with digital cytology has been growing gradually. Therefore, there is a need to evaluate the current state of digital cytology. This study aimed to determine the current landscape of digital cytology via a survey conducted as part of the American Society of Cytopathology (ASC) Digital Cytology White Paper Task Force.

MATERIALS AND METHODS

A survey with 43 questions pertaining to the current practices and experiences of WSI and AI in both surgical pathology and cytology was created. The survey was sent to members of the ASC, the International Academy of Cytology (IAC), and the Papanicolaou Society of Cytopathology (PSC). Responses were recorded and analyzed.

RESULTS

In total, 327 individuals participated in the survey, spanning a diverse array of practice settings, roles, and experiences around the globe. The majority of responses indicated there was routine scanning of surgical pathology slides (n = 134; 61%) with fewer respondents scanning cytology slides (n = 150; 46%). The primary challenge for surgical WSI is the need for faster scanning and cost minimization, whereas image quality is the top issue for cytology WSI. AI tools are not widely utilized, with only 16% of participants using AI for surgical pathology samples and 13% for cytology practice.

CONCLUSIONS

Utilization of digital pathology is limited in cytology laboratories as compared to surgical pathology. However, as more laboratories are willing to implement digital cytology in the near future, the establishment of practical clinical guidelines is needed.

Habitat escalated adaptive therapy (HEAT): a phase 2 trial utilizing radiomic habitat-directed and genomic-adjusted radiation dose (GARD) optimization for high-grade soft tissue sarcoma

BACKGROUND

Soft tissue sarcomas (STS), have significant inter- and intra-tumoral heterogeneity, with poor response to standard neoadjuvant radiotherapy (RT). Achieving a favorable pathologic response (FPR ≥ 95%) from RT is associated with improved patient outcome. Genomic adjusted radiation dose (GARD), a radiation-specific metric that quantifies the expected RT treatment effect as a function of tumor dose and genomics, proposed that STS is significantly underdosed. STS have significant radiomic heterogeneity, where radiomic habitats can delineate regions of intra-tumoral hypoxia and radioresistance. We designed a novel clinical trial, Habitat Escalated Adaptive Therapy (HEAT), utilizing radiomic habitats to identify areas of radioresistance within the tumor and targeting them with GARD-optimized doses, to improve FPR in high-grade STS.

METHODS

Phase 2 non-randomized single-arm clinical trial includes non-metastatic, resectable high-grade STS patients. Pre-treatment multiparametric MRIs (mpMRI) delineate three distinct intra-tumoral habitats based on apparent diffusion coefficient (ADC) and dynamic contrast enhanced (DCE) sequences. GARD estimates that simultaneous integrated boost (SIB) doses of 70 and 60 Gy in 25 fractions to the highest and intermediate radioresistant habitats, while the remaining volume receives standard 50 Gy, would lead to a > 3 fold FPR increase to 24%. Pre-treatment CT guided biopsies of each habitat along with clip placement will be performed for pathologic evaluation, future genomic studies, and response assessment. An mpMRI taken between weeks two and three of treatment will be used for biological plan adaptation to account for tumor response, in addition to an mpMRI after the completion of radiotherapy in addition to pathologic response, toxicity, radiomic response, disease control, and survival will be evaluated as secondary endpoints. Furthermore, liquid biopsy will be performed with mpMRI for future ancillary studies.

DISCUSSION

This is the first clinical trial to test a novel genomic-based RT dose optimization (GARD) and to utilize radiomic habitats to identify and target radioresistance regions, as a strategy to improve the outcome of RT-treated STS patients. Its success could usher in a new phase in radiation oncology, integrating genomic and radiomic insights into clinical practice and trial designs, and may reveal new radiomic and genomic biomarkers, refining personalized treatment strategies for STS.

TRIAL REGISTRATION

NCT05301283.

TRIAL STATUS

The trial started recruitment on March 17, 2022.

Digital cytology part 2: artificial intelligence in cytology: a concept paper with review and recommendations from the American Society of Cytopathology Digital Cytology Task Force

Digital cytology and artificial intelligence (AI) are gaining greater adoption in the cytology laboratory. However, peer-reviewed real-world data and literature are lacking in regard to the current clinical landscape. The American Society of Cytopathology in conjunction with the International Academy of Cytology and the Digital Pathology Association established a special task force comprising 20 members with expertise and/or interest in digital cytology. The aim of the group was to investigate the feasibility of incorporating digital cytology, specifically cytology whole slide scanning and AI applications, into the workflow of the laboratory. In turn, the impact on cytopathologists, cytologists (cytotechnologists), and cytology departments were also assessed. The task force reviewed existing literature on digital cytology, conducted a worldwide survey, and held a virtual roundtable discussion on digital cytology and AI with multiple industry corporate representatives. This white paper, presented in 2 parts, summarizes the current state of digital cytology and AI practice in global cytology practice. Part 1 of the white paper is presented as a separate paper which details a review and best practice recommendations for incorporating digital cytology into practice. Part 2 of the white paper presented here provides a comprehensive review of AI in cytology practice along with best practice recommendations and legal considerations. Additionally, the cytology global survey results highlighting current AI practices by various laboratories, as well as current attitudes, are reported.

Digital cytology part 1: digital cytology implementation for practice: a concept paper with review and recommendations from the American Society of Cytopathology Digital Cytology Task Force

Digital cytology and artificial intelligence (AI) are gaining greater adoption in the cytopathology laboratory. However, peer-reviewed real-world data and literature are lacking regarding the current clinical landscape. The American Society of Cytopathology in conjunction with the International Academy of Cytology and the Digital Pathology Association established a special task force comprising 20 members with expertise and/or interest in digital cytology. The aim of the group was to investigate the feasibility of incorporating digital cytology, specifically cytology whole slide scanning and AI applications, into the workflow of the laboratory. In turn, the impact on cytopathologists, cytologists (cytotechnologists), and cytology departments were also assessed. The task force reviewed existing literature on digital cytology, conducted a worldwide survey, and held a virtual roundtable discussion on digital cytology and AI with multiple industry corporate representatives. This white paper, presented in 2 parts, summarizes the current state of digital cytology and AI practice in global cytology practice. Part 1 of the white paper presented herein is a review and offers best practice recommendations for incorporating digital cytology into practice. Part 2 of the white paper provides a comprehensive review of AI in cytology practice along with best practice recommendations and legal considerations. Additionally, the results of a global survey regarding digital cytology are highlighted.

Emerging Landscape of Targeted Therapy of Breast Cancers With Low Human Epidermal Growth Factor Receptor 2 Protein Expression

CONTEXT.—

Human epidermal growth factor receptor 2 (HER2) status in breast cancer is currently classified as negative or positive for selecting patients for anti-HER2 targeted therapy. The evolution of the HER2 status has included a new HER2-low category defined as an HER2 immunohistochemistry score of 1+ or 2+ without gene amplification. This new category opens the door to a targetable HER2-low breast cancer population for which new treatments may be effective.

OBJECTIVE.—

To review the current literature on the emerging category of breast cancers with low HER2 protein expression, including the clinical, histopathologic, and molecular features, and outline the clinical trials and best practice recommendations for identifying HER2-low-expressing breast cancers by immunohistochemistry.

DATA SOURCES.—

We conducted a literature review based on peer-reviewed original articles, review articles, regulatory communications, ongoing and past clinical trials identified through ClinicalTrials.gov, and the authors' practice experience.

CONCLUSIONS.—

The availability of new targeted therapy potentially effective for patients with breast cancers with low HER2 protein expression requires multidisciplinary recognition. In particular, pathologists need to recognize and identify this category to allow the optimal selection of patients for targeted therapy.

Recommendations for Performance Evaluation of Machine Learning in Pathology: A Concept Paper From the College of American Pathologists

CONTEXT.—

Machine learning applications in the pathology clinical domain are emerging rapidly. As decision support systems continue to mature, laboratories will increasingly need guidance to evaluate their performance in clinical practice. Currently there are no formal guidelines to assist pathology laboratories in verification and/or validation of such systems. These recommendations are being proposed for the evaluation of machine learning systems in the clinical practice of pathology.

OBJECTIVE.—

To propose recommendations for performance evaluation of in vitro diagnostic tests on patient samples that incorporate machine learning as part of the preanalytical, analytical, or postanalytical phases of the laboratory workflow. Topics described include considerations for machine learning model evaluation including risk assessment, predeployment requirements, data sourcing and curation, verification and validation, change control management, human-computer interaction, practitioner training, and competency evaluation.

DATA SOURCES.—

An expert panel performed a review of the literature, Clinical and Laboratory Standards Institute guidance, and laboratory and government regulatory frameworks.

CONCLUSIONS.—

Review of the literature and existing documents enabled the development of proposed recommendations. This white paper pertains to performance evaluation of machine learning systems intended to be implemented for clinical patient testing. Further studies with real-world clinical data are encouraged to support these proposed recommendations. Performance evaluation of machine learning models is critical to verification and/or validation of in vitro diagnostic tests using machine learning intended for clinical practice.

Stroma-Mediated Breast Cancer Cell Proliferation Indirectly Drives Chemoresistance by Accelerating Tumor Recovery between Chemotherapy Cycles

The ability of tumors to survive therapy reflects both cell-intrinsic and microenvironmental mechanisms. Across many cancers, including triple-negative breast cancer (TNBC), a high stroma/tumor ratio correlates with poor survival. In many contexts, this correlation can be explained by the direct reduction of therapy sensitivity induced by stroma-produced paracrine factors. We sought to explore whether this direct effect contributes to the link between stroma and poor responses to chemotherapies. In vitro studies with panels of TNBC cell line models and stromal isolates failed to detect a direct modulation of chemoresistance. At the same time, consistent with prior studies, fibroblast-produced secreted factors stimulated treatment-independent enhancement of tumor cell proliferation. Spatial analyses indicated that proximity to stroma is often associated with enhanced tumor cell proliferation in vivo. These observations suggested an indirect link between stroma and chemoresistance, where stroma-augmented proliferation potentiates the recovery of residual tumors between chemotherapy cycles. To evaluate this hypothesis, a spatial agent-based model of stroma impact on proliferation/death dynamics was developed that was quantitatively parameterized using inferences from histologic analyses and experimental studies. The model demonstrated that the observed enhancement of tumor cell proliferation within stroma-proximal niches could enable tumors to avoid elimination over multiple chemotherapy cycles. Therefore, this study supports the existence of an indirect mechanism of environment-mediated chemoresistance that might contribute to the negative correlation between stromal content and poor therapy outcomes.

SIGNIFICANCE

Integration of experimental research with mathematical modeling reveals an indirect microenvironmental chemoresistance mechanism by which stromal cells stimulate breast cancer cell proliferation and highlights the importance of consideration of proliferation/death dynamics. See related commentary by Wall and Echeverria, p. 3667.

Revolutionizing Digital Pathology With the Power of Generative Artificial Intelligence and Foundation Models

Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow. Whole-slide imaging allows pathologists to view and analyze microscopic images on a computer monitor, enabling computational pathology. By leveraging artificial intelligence (AI) and machine learning (ML), computational pathology has emerged as a promising field in recent years. Recently, task-specific AI/ML (eg, convolutional neural networks) has risen to the forefront, achieving above-human performance in many image-processing and computer vision tasks. The performance of task-specific AI/ML models depends on the availability of many annotated training datasets, which presents a rate-limiting factor for AI/ML development in pathology. Task-specific AI/ML models cannot benefit from multimodal data and lack generalization, eg, the AI models often struggle to generalize to new datasets or unseen variations in image acquisition, staining techniques, or tissue types. The 2020s are witnessing the rise of foundation models and generative AI. A foundation model is a large AI model trained using sizable data, which is later adapted (or fine-tuned) to perform different tasks using a modest amount of task-specific annotated data. These AI models provide in-context learning, can self-correct mistakes, and promptly adjust to user feedback. In this review, we provide a brief overview of recent advances in computational pathology enabled by task-specific AI, their challenges and limitations, and then introduce various foundation models. We propose to create a pathology-specific generative AI based on multimodal foundation models and present its potentially transformative role in digital pathology. We describe different use cases, delineating how it could serve as an expert companion of pathologists and help them efficiently and objectively perform routine laboratory tasks, including quantifying image analysis, generating pathology reports, diagnosis, and prognosis. We also outline the potential role that foundation models and generative AI can play in standardizing the pathology laboratory workflow, education, and training.

Validation of PRKCB Immunohistochemistry as a Biomarker for the Diagnosis of Ewing Sarcoma

Background: Ewing sarcoma (ES) can be confirmed by identifying the EWSR1-FLI1 fusion transcript. This study is to investigate whether immunostaining (IHC) of PRKCB-a protein directly regulated by EWSR1-FLI1 is a surrogate maker for diagnosing ES in routine practice. Methods: Microarray gene expression analyses were conducted. RKCB IHC was applied to 69 ES confirmed by morphology and molecular methods, and 41 non-Ewing small round cell tumors. EWSR1 rearrangement, EWSR1-FLI1 fusion or t(11;22)(q24;q12) were identified by fluorescence in situ hybridization, reverse transcriptase polymerase chain reaction, or cytogenetic analysis, respectively. Results: Gene array analyses showed significant overexpression of the PRKCB in ES. PRKCB IHC was positive in 19 cases of ES with EWSR1-FLI1 fusion, 3 cases with cytogenetic 11:22 translocation and 59 cases with EWSR1 rearrangement while negative in only one EWSR1 rearranged case. PRKCB IHC is sensitive (98%) and specific (96%) in detecting EWSR1 rearranged ES. Conclusions: PRKCB is a reliable antibody for diagnosing ES in routine practice.

Integrative diagnostics: the time is now-a report from the International Society for Strategic Studies in Radiology

Enormous recent progress in diagnostic testing can enable more accurate diagnosis and improved clinical outcomes. Yet these tests are increasingly challenging and frustrating; the volume and diversity of results may overwhelm the diagnostic acumen of even the most dedicated and experienced clinician. Because they are gathered and processed within the "silo" of each diagnostic discipline, diagnostic data are fragmented, and the electronic health record does little to synthesize new and existing data into usable information. Therefore, despite great promise, diagnoses may still be incorrect, delayed, or never made. Integrative diagnostics represents a vision for the future, wherein diagnostic data, together with clinical data from the electronic health record, are aggregated and contextualized by informatics tools to direct clinical action. Integrative diagnostics has the potential to identify correct therapies more quickly, modify treatment when appropriate, and terminate treatment when not effective, ultimately decreasing morbidity, improving outcomes, and avoiding unnecessary costs. Radiology, laboratory medicine, and pathology already play major roles in medical diagnostics. Our specialties can increase the value of our examinations by taking a holistic approach to their selection, interpretation, and application to the patient's care pathway. We have the means and rationale to incorporate integrative diagnostics into our specialties and guide its implementation in clinical practice.

Genomic Patterns of Malignant Peripheral Nerve Sheath Tumor (MPNST) Evolution Correlate with Clinical Outcome and Are Detectable in Cell-Free DNA

Malignant peripheral nerve sheath tumor (MPNST), an aggressive soft-tissue sarcoma, occurs in people with neurofibromatosis type 1 (NF1) and sporadically. Whole-genome and multiregional exome sequencing, transcriptomic, and methylation profiling of 95 tumor samples revealed the order of genomic events in tumor evolution. Following biallelic inactivation of NF1, loss of CDKN2A or TP53 with or without inactivation of polycomb repressive complex 2 (PRC2) leads to extensive somatic copy-number aberrations (SCNA). Distinct pathways of tumor evolution are associated with inactivation of PRC2 genes and H3K27 trimethylation (H3K27me3) status. Tumors with H3K27me3 loss evolve through extensive chromosomal losses followed by whole-genome doubling and chromosome 8 amplification, and show lower levels of immune cell infiltration. Retention of H3K27me3 leads to extensive genomic instability, but an immune cell-rich phenotype. Specific SCNAs detected in both tumor samples and cell-free DNA (cfDNA) act as a surrogate for H3K27me3 loss and immune infiltration, and predict prognosis.

SIGNIFICANCE

MPNST is the most common cause of death and morbidity for individuals with NF1, a relatively common tumor predisposition syndrome. Our results suggest that somatic copy-number and methylation profiling of tumor or cfDNA could serve as a biomarker for early diagnosis and to stratify patients into prognostic and treatment-related subgroups. This article is highlighted in the In This Issue feature, p. 517.

Paracrine enhancement of tumor cell proliferation provides indirect stroma-mediated chemoresistance via acceleration of tumor recovery between chemotherapy cycles

The ability of tumors to survive therapy is mediated not only by cell-intrinsic but also cell-extrinsic, microenvironmental mechanisms. Across many cancers, including triple-negative breast cancer (TNBC), a high stroma/tumor ratio correlates with poor survival. In many contexts, this correlation can be explained by the direct reduction of therapy sensitivity by stroma-produced paracrine factors through activating pro-survival signaling and stemness. We sought to explore whether this direct effect contributes to the link between stroma and poor responses to chemotherapies in TNBC. Our in vitro studies with panels of TNBC cell line models and stromal isolates failed to detect a direct modulation of chemoresistance. However, we found that fibroblasts often enhance baseline tumor cell proliferation. Consistent with this in vitro observation, we found evidence of stroma-enhanced TNBC cell proliferation in vivo , in xenograft models and patient samples. Based on these observations, we hypothesized an indirect link between stroma and chemoresistance, where stroma-augmented proliferation potentiates the recovery of residual tumors between chemotherapy cycles. To test this hypothesis, we developed a spatial agent-based model of tumor response to repeated dosing of chemotherapy. The model was quantitatively parameterized from histological analyses and experimental studies. We found that even a slight enhancement of tumor cell proliferation within stroma-proximal niches can strongly enhance the ability of tumors to survive multiple cycles of chemotherapy under biologically and clinically feasible parameters. In summary, our study uncovered a novel, indirect mechanism of chemoresistance. Further, our study highlights the limitations of short-term cytotoxicity assays in understanding chemotherapy responses and supports the integration of experimental and in silico modeling.

Integrative Diagnostics: The Time Is Now-A Report From the International Society for Strategic Studies in Radiology

Enormous recent progress in diagnostic testing can enable more accurate diagnosis and improved clinical outcomes. Yet these tests are increasingly challenging and frustrating; the volume and diversity of results may overwhelm the diagnostic acumen of even the most dedicated and experienced clinician. Because they are gathered and processed within the "silo" of each diagnostic discipline, diagnostic data are fragmented, and the electronic health record does little to synthesize new and existing data into usable information. Therefore, despite great promise, diagnoses may still be incorrect, delayed, or never made. Integrative diagnostics represents a vision for the future, wherein diagnostic data, together with clinical data from the electronic health record, are aggregated and contextualized by informatics tools to direct clinical action. Integrative diagnostics has the potential to identify correct therapies more quickly, modify treatment when appropriate, and terminate treatment when not effective, ultimately decreasing morbidity, improving outcomes, and avoiding unnecessary costs. Radiology, laboratory medicine, and pathology already play major roles in medical diagnostics. Our specialties can increase the value of our examinations by taking a holistic approach to their selection, interpretation, and application to the patient's care pathway. We have the means and rationale to incorporate integrative diagnostics into our specialties and guide its implementation in clinical practice.

NCCN Guidelines® Insights: Gastrointestinal Stromal Tumors, Version 2.2022

Gastrointestinal stromal tumors (GIST) are the most common type of soft tissue sarcoma that occur throughout the gastrointestinal tract. Most of these tumors are caused by oncogenic activating mutations in the KIT or PDGFRA genes. The NCCN Guidelines for GIST provide recommendations for the diagnosis, evaluation, treatment, and follow-up of patients with these tumors. These NCCN Guidelines Insights summarize the panel discussion behind recent important updates to the guidelines, including revised systemic therapy options for unresectable, progressive, or metastatic GIST based on mutational status, and updated recommendations for the management of GIST that develop resistance to specific tyrosine kinase inhibitors.

Assessing ROSE for adequacy of EBUS-TBNA compared with a direct-to-cell block approach as a response to the COVID-19 pandemic

Introduction:

Rapid on-site evaluation (ROSE) has been used during the endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) procedure as standard practice. Because of the COVID-19 (coronavirus disease 2019) pandemic, our institute had had to discontinue ROSE and adopt a direct-to-cell block approach. In the present study, we aimed to determine whether this change has had significant effects on the cytopathology quality.

Materials and methods:

A total of 1903 EBUS-TBNA cases from 734 patients were collected (1097 cases with ROSE for 452 patients; 806 cases without ROSE but with direct-to-cell block for 282 patients). The clinical and cytology data were analyzed using SAS, version 9.4, software to render calculated standardized residuals and a fitted multivariate generalized linear model.

Results:

On average, a biopsy from a patient with ROSE was 0.936 (=exp −0.066) times less likely to be reported as satisfactory compared with a biopsy from a patient without ROSE, although the difference was not statistically significant (P = 0.785). The inadequacy rate of EBUS-TBNA was 6.4% higher on average for cases with ROSE compared with a direct-to-cell block approach. However, this difference was also not statistically significant. The proportions of biopsies reported as diagnostic for malignancy and other were significantly different between the ROSE and no-ROSE groups with a standardized residual of 1.80 (P = 0.036) and −2.27 (P = 0.012), respectively.

Conclusions

Discontinuing ROSE and using a direct-to-cell block approach had no negative effects on cytopathology quality. This practice can be considered acceptable during the COVID-19 pandemic when social distancing and the shortage of staff and supplies have resulted in challenges to delivering quality care to cancer patients whose treatment cannot be postponed.

Proton export upregulates aerobic glycolysis

INTRODUCTION

Aggressive cancers commonly ferment glucose to lactic acid at high rates, even in the presence of oxygen. This is known as aerobic glycolysis, or the "Warburg Effect." It is widely assumed that this is a consequence of the upregulation of glycolytic enzymes. Oncogenic drivers can increase the expression of most proteins in the glycolytic pathway, including the terminal step of exporting H+ equivalents from the cytoplasm. Proton exporters maintain an alkaline cytoplasmic pH, which can enhance all glycolytic enzyme activities, even in the absence of oncogene-related expression changes. Based on this observation, we hypothesized that increased uptake and fermentative metabolism of glucose could be driven by the expulsion of H+ equivalents from the cell.

RESULTS

To test this hypothesis, we stably transfected lowly glycolytic MCF-7, U2-OS, and glycolytic HEK293 cells to express proton-exporting systems: either PMA1 (plasma membrane ATPase 1, a yeast H+-ATPase) or CA-IX (carbonic anhydrase 9). The expression of either exporter in vitro enhanced aerobic glycolysis as measured by glucose consumption, lactate production, and extracellular acidification rate. This resulted in an increased intracellular pH, and metabolomic analyses indicated that this was associated with an increased flux of all glycolytic enzymes upstream of pyruvate kinase. These cells also demonstrated increased migratory and invasive phenotypes in vitro, and these were recapitulated in vivo by more aggressive behavior, whereby the acid-producing cells formed higher-grade tumors with higher rates of metastases. Neutralizing tumor acidity with oral buffers reduced the metastatic burden.

CONCLUSIONS

Therefore, cancer cells which increase export of H+ equivalents subsequently increase intracellular alkalization, even without oncogenic driver mutations, and this is sufficient to alter cancer metabolism towards an upregulation of aerobic glycolysis, a Warburg phenotype. Overall, we have shown that the traditional understanding of cancer cells favoring glycolysis and the subsequent extracellular acidification is not always linear. Cells which can, independent of metabolism, acidify through proton exporter activity can sufficiently drive their metabolism towards glycolysis providing an important fitness advantage for survival.

Soft Tissue Sarcoma, Version 2.2022, NCCN Clinical Practice Guidelines in Oncology

Soft tissue sarcomas (STS) are rare malignancies of mesenchymal cell origin that display a heterogenous mix of clinical and pathologic characteristics. STS can develop from fat, muscle, nerves, blood vessels, and other connective tissues. The evaluation and treatment of patients with STS requires a multidisciplinary team with demonstrated expertise in the management of these tumors. The complete NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Soft Tissue Sarcoma provide recommendations for the diagnosis, evaluation, and treatment of extremity/superficial trunk/head and neck STS, as well as retroperitoneal/intra-abdominal STS, desmoid tumors, and rhabdomyosarcoma. This portion of the NCCN Guidelines discusses general principles for the diagnosis and treatment of retroperitoneal/intra-abdominal STS, outlines treatment recommendations, and reviews the evidence to support the guidelines recommendations.

Lipogenesis mediated by OGR1 regulates metabolic adaptation to acid stress in cancer cells via autophagy

Malignant tumors exhibit altered metabolism resulting in a highly acidic extracellular microenvironment. Here, we show that cytoplasmic lipid droplet (LD) accumulation, indicative of a lipogenic phenotype, is a cellular adaption to extracellular acidity. LD marker PLIN2 is strongly associated with poor overall survival in breast cancer patients. Acid-induced LD accumulation is triggered by activation of the acid-sensing G-protein-coupled receptor (GPCR) OGR1, which is expressed highly in breast tumors. OGR1 depletion inhibits acid-induced lipid accumulation, while activation by a synthetic agonist triggers LD formation. Inhibition of OGR1 downstream signaling abrogates the lipogenic phenotype, which can be rescued with OGR1 ectopic expression. OGR1-depleted cells show growth inhibition under acidic growth conditions in vitro and tumor formation in vivo. Isotope tracing shows that the source of lipid precursors is primarily autophagy-derived ketogenic amino acids. OGR1-depleted cells are defective in endoplasmic reticulum stress response and autophagy and hence fail to accumulate LDs affecting survival under acidic stress.

Validating Whole Slide Imaging Systems for Diagnostic Purposes in Pathology

CONTEXT.—

The original guideline, "Validating Whole Slide Imaging for Diagnostic Purposes in Pathology," was published in 2013 and included 12 guideline statements. The College of American Pathologists convened an expert panel to update the guideline following standards established by the National Academies of Medicine for developing trustworthy clinical practice guidelines.

OBJECTIVE.—

To assess evidence published since the release of the original guideline and provide updated recommendations for validating whole slide imaging (WSI) systems used for diagnostic purposes.

DESIGN.—

An expert panel performed a systematic review of the literature. Frozen sections, anatomic pathology specimens (biopsies, curettings, and resections), and hematopathology cases were included. Cytology cases were excluded. Using the Grading of Recommendations Assessment, Development, and Evaluation approach, the panel reassessed and updated the original guideline recommendations.

RESULTS.—

Three strong recommendations and 9 good practice statements are offered to assist laboratories with validating WSI digital pathology systems.

CONCLUSIONS.—

Systematic review of literature following release of the 2013 guideline reaffirms the use of a validation set of at least 60 cases, establishing intraobserver diagnostic concordance between WSI and glass slides and the use of a 2-week washout period between modalities. Although all discordances between WSI and glass slide diagnoses discovered during validation need to be reconciled, laboratories should be particularly concerned if their overall WSI-glass slide concordance is less than 95%.

OUP accepted manuscript

Background

Cancer of unknown primary (CUP) comprises a heterogeneous collection of malignancies that are typically associated with a poor prognosis and a lack of effective treatment options. We retrospectively evaluated the clinical utility of targeted next-generation sequencing (NGS) among CUP patients to assist with diagnosis and identify opportunities for molecularly guided therapy.

Patients and Methods

Patients with a CUP at Moffitt Cancer Center who underwent NGS between January 1, 2014 and December 31, 2019, were eligible for study inclusion. Next-generation sequencing results were assessed to determine the frequency of clinically actionable molecular alterations, and chart reviews were performed to ascertain the number of patients receiving molecularly guided therapy.

Results

Ninety-five CUP patients were identified for analysis. Next-generation sequencing testing identified options for molecularly guided therapy for 55% (n = 52) of patients. Among patients with molecularly guided therapy options, 33% (n = 17) were prescribed a molecularly guided therapy. The median overall survival for those receiving molecularly guided therapy was 23.6 months. Among the evaluable patients, the median duration of treatment for CUP patients (n = 7) receiving molecular-guided therapy as a first-line therapy was 39 weeks. The median duration of treatment for CUP patients (n = 8) treated with molecularly guided therapy in the second- or later-line setting was 13 weeks. Next-generation sequencing results were found to be suggestive of a likely primary tumor type for 15% (n = 14) of patients.

Conclusion

Next-generation sequencing results enabled the identification of treatment options in a majority of patients and assisted with the identification of a likely primary tumor type in a clinically meaningful subset of patients.

Bridging the Gap: The Critical Role of Regulatory Affairs and Clinical Affairs in the Total Product Life Cycle of Pathology Imaging Devices and Software

Manufacturers of pathology imaging devices and associated software engage regulatory affairs and clinical affairs (RACA) throughout the Total Product Life Cycle (TPLC) of regulated products. A number of manufacturers, pathologists, and end users are not familiar with how RACA involvement benefits each stage of the TPLC. RACA professionals are important contributors to product development and deployment strategies because these professionals maintain an understanding of the scientific, technical, and clinical aspects of biomedical product regulation, as well as the relevant knowledge of regulatory requirements, policies, and market trends for both local and global regulations and standards. Defining a regulatory and clinical strategy at the beginning of product design enables early evaluation of risks and provides assurance that the collected evidence supports the product's clinical claims (e.g., in a marketing application), its safe and effective use, and potential reimbursement strategies. It is recommended to involve RACA early and throughout the TPLC to assist with navigating changes in the regulatory environment and dynamic diagnostic market. Here we outline how various stakeholders can utilize RACA to navigate the nuanced landscape behind the development and use of clinical diagnostic products. Collectively, this work emphasizes the critical importance of RACA as an integral part of product development and, thereby, sustained innovation.