SignificanceAccurate identification of residual tumor during head and neck cancer surgery is essential for preventing local recurrences and improving patient outcomes, yet current methods for intraoperative margin assessment remain limited. Fluorescence lifetime imaging (FLIm), a label-free optical technique, has shown strong potential for distinguishing cancerous from noncancerous tissue in vivo. However, the effect of tissue resection on autofluorescence properties is not well defined, raising the question of whether diagnostic signatures establishedin vivo translate reliably to ex vivo specimens.AimThe aim is to characterize how fluorescence lifetime properties change between in vivo and ex vivo conditions across multiple head and neck anatomical sites and to assess how these changes affect diagnostic separability between cancerous and noncancerous tissue.ApproachA custom fiber-based, point-scanning FLIm system with phasor-based analysis was used to characterize fluorescence lifetime features in vivo and ex vivo across oral tongue, base of the tongue, and palatine tonsil tissue from 75 patients undergoing oropharyngeal surgery. A separability index based on phasor distributions was used to quantify cancer versus noncancer contrast across multiple spectral channels.ResultsResection-induced changes in decay dynamics and fluorescence lifetime values varied by anatomy: oral tongue and tonsil showed reduced contrast ex vivo, whereas the base of tongue demonstrated improved separation after excision. The base of the tongue showed a higher separability index ex vivo (0.43 versus 0.15 in vivo), whereas oral tongue and tonsil maintained superior contrast in vivo (0.27 versus 0.18 and 0.46 versus 0.28, respectively).ConclusionsSurgical resection substantially alters autofluorescence signatures in an anatomy-dependent manner, emphasizing the need to train and validate diagnostic algorithms independently for in vivo surgical guidance and ex vivo pathology assessment.

Precise intraoperative delineation of tumor margin is critical for maximizing resection completeness and minimizing recurrence in head and neck cancers (HNC). Label-free Fluorescence Lifetime Imaging (FLIm), which captures the fluorescence decay characteristics of endogenous molecules, offers a real-time method for differentiating malignant from healthy tissue during surgery without the need of contrast agents. Here, we present a data-centric artificial intelligence (AI) framework to enhance the robustness and accuracy of FLIm-based classification models for HNC. FLIm data were collected in vivo from 92 patients undergoing both transoral robotic surgery (TORS) and non-TORS procedures for HNC using a multispectral FLIm device with 355 nm excitation. To improve model performance, a data-centric approach leveraging confident learning was implemented to identify and exclude instances with low quality. An interpretability framework was further integrated to quantify feature contributions and elucidate FLIm-derived sources of contrast. Under a leave-one-patient-out cross-validation scheme, the model demonstrated a strong discriminative ability with an area under the receiver operating characteristic curve of 0.94 in differentiating healthy versus cancerous tissue. In tumor boundary regions, borderline predictions revealed transitional tissue properties, with strong correlations observed between model predictions and FLIm parameters in spectral channels corresponding to NADH and FAD-key metabolic cofactors indicative of cellular metabolic shifts at tumor margins. The impact of tumor anatomical site (base of tongue, palatine tonsil, oral tongue) and p16+ (HPV) status on classification performance was also assessed. These findings underscore the potential of label-free FLIm to provide accurate, real-time guidance for intraoperative margin assessment and dysplasia grading, advancing surgical precision in head and neck surgical oncology.

CONTEXT.—:The adoption of digital pathology may enable pathologists to perform primary diagnosis in both local and remote whole slide image viewing settings, improving logistics and convenience. OBJECTIVE.—:To test the performance of a new whole slide imaging system (Aperio GT 450 DX), both local intranet-based and remote internet-based viewing were compared with manual glass slide light microscopy. DESIGN.—:A total of 1161 curated cases, enriched with difficult clinical diagnoses, were enrolled in this accuracy study and digitally scanned on 3 Aperio GT 450 DX instruments at 3 clinical sites. Ten reading pathologists across the 3 study sites viewed images either locally (directly connected to the image server) or remotely (viewed over an internet connection). Each diagnosis was scored (concordant, minor discrepancy, or major discrepancy) by a separate team of 3 adjudication pathologists. The diagnostic accuracy of the Aperio GT 450 DX was tested by comparing the whole slide image review diagnosis with the conventional light microscope manual slide review diagnosis. RESULTS.—:The difference in the major discrepancy rate between whole slide image review diagnosis and manual slide review diagnosis was 2.40% (95% CI, 1.40%-3.39%), meeting the predefined acceptance criterion of the 95% CI upper bound of 4% or less. Secondary end points were also met, including an upper bound of 7% or less and both local-only and remote-only upper-bound discrepancy rates of 4% or less. Major discrepancies were slightly lower for the remotely viewed cases (2.17%) compared with local direct server connection (2.61%), and time per read was not different. CONCLUSIONS.—:The diagnoses made using the Aperio GT 450 DX, using both local and remote access image data, were noninferior to the diagnoses made using conventional light microscopy.

Employing label-free fluorescence lifetime imaging for cancer margin delineation during oropharyngeal resection surgery is impeded by molecular alterations from risk factors (alcohol, tobacco, HPV). Improved discrimination is observed in HPV-negative cancer compared to HPV-positive cases.

Objectives: Early detection and accurate diagnosis of lymph node metastasis (LNM) in head and neck cancer (HNC) are crucial for enhancing patient prognosis and survival rates. Current imaging methods have limitations, necessitating new evaluation of new diagnostic techniques. This study investigates the potential of combining pre-operative CT and intra-operative fluorescence lifetime imaging (FLIm) to enhance LNM prediction in HNC using primary tumor signatures. Methods: CT and FLIm data were collected from 46 HNC patients. A total of 42 FLIm features and 924 CT radiomic features were extracted from the primary tumor site and fused. A support vector machine (SVM) model with a radial basis function kernel was trained to predict LNM. Hyperparameter tuning was conducted using 10-fold nested cross-validation. Prediction performance was evaluated using balanced accuracy (bACC) and the area under the ROC curve (AUC). Results: The model, leveraging combined CT and FLIm features, demonstrated improved testing accuracy (bACC: 0.71, AUC: 0.79) over the CT-only (bACC: 0.58, AUC: 0.67) and FLIm-only (bACC: 0.61, AUC: 0.72) models. Feature selection identified that a subset of 10 FLIm and 10 CT features provided optimal predictive capability. Feature contribution analysis identified high-pass and low-pass wavelet-filtered CT images as well as Laguerre coefficients from FLIm as key predictors. Conclusions: Combining CT and FLIm of the primary tumor improves the prediction of HNC LNM compared to either modality alone. Significance: This study underscores the potential of combining pre-operative radiomics with intra-operative FLIm for more accurate LNM prediction in HNC, offering promise to enhance patient outcomes.

Supplementary Table S1 from Effect of Altering Dietary ω-6/ω-3 Fatty Acid Ratios on Prostate Cancer Membrane Composition, Cyclooxygenase-2, and Prostaglandin E2

Supplementary Figure 1 from Effect of Low-Fat Diet on Development of Prostate Cancer and Akt Phosphorylation in the Hi-Myc Transgenic Mouse Model

Incomplete surgical resection with residual cancer left in the surgical cavity is a potential sequelae of Transoral Robotic Surgery (TORS). To minimize such risk, surgeons rely on intraoperative frozen sections analysis (IFSA) to locate and remove the remaining tumor. This process, may lead to false negatives and is time-consuming. Mesoscopic fluorescence lifetime imaging (FLIm) of tissue fluorophores (i.e., collagen and metabolic co-factors NADH and FAD) emission has demonstrated the potential to demarcate the extent of head and neck cancer in patients undergoing surgical procedures of the oral cavity and the oropharynx. Here, we demonstrate the first label-free FLIm-based classification using a novelty detection model to identify residual cancer in the surgical cavity of the oropharynx. Due to highly imbalanced label representation in the surgical cavity, the model employed solely FLIm data from healthy surgical cavity tissue for training and classified the residual tumors as an anomaly. FLIm data from N = 22 patients undergoing upper aerodigestive oncologic surgery were used to train and validate the classification model using leave-one-patient-out cross-validation. Our approach identified all patients with positive surgical margins (N = 3) confirmed by pathology. Furthermore, the proposed method reported a point-level sensitivity of 0.75 and a specificity of 0.78 across optically interrogated tissue surface for all N = 22 patients. The results indicate that the FLIm-based classification model can identify residual cancer by directly imaging the surgical cavity, potentially enabling intraoperative surgical guidance for TORS.

Video 1Cholangioscopic examination of the ampullary channel and extrahepatic bile duct.

Intraoperative identification of head and neck cancer tissue is essential to achieve complete tumor resection and mitigate tumor recurrence. Mesoscopic fluorescence lifetime imaging (FLIm) of intrinsic tissue fluorophores emission has demonstrated the potential to demarcate the extent of the tumor in patients undergoing surgical procedures of the oral cavity and the oropharynx. Here, we report FLIm-based classification methods using standard machine learning models that account for the diverse anatomical and biochemical composition across the head and neck anatomy to improve tumor region identification. Three anatomy-specific binary classification models were developed (i.e., “base of tongue,” “palatine tonsil,” and “oral tongue”). FLIm data from patients (N = 85) undergoing upper aerodigestive oncologic surgery were used to train and validate the classification models using a leave-one-patient-out cross-validation method. These models were evaluated for two classification tasks: (1) to discriminate between healthy and cancer tissue, and (2) to apply the binary classification model trained on healthy and cancer to discriminate dysplasia through transfer learning. This approach achieved superior classification performance compared to models that are anatomy-agnostic; specifically, a ROC-AUC of 0.94 was for the first task and 0.92 for the second. Furthermore, the model demonstrated detection of dysplasia, highlighting the generalization of the FLIm-based classifier. Current findings demonstrate that a classifier that accounts for tumor location can improve the ability to accurately identify surgical margins and underscore FLIm's potential as a tool for surgical guidance in head and neck cancer patients, including those subjects of robotic surgery.

BACKGROUND:This study evaluated whether fluorescence lifetime imaging (FLIm), coupled with standard diagnostic workups, could enhance primary lesion detection in patients with p16+ head and neck squamous cell carcinoma of the unknown primary (HNSCCUP). METHODS:FLIm was integrated into transoral robotic surgery to acquire optical data on six HNSCCUP patients' oropharyngeal tissues. An additional 55-patient FLIm dataset, comprising conventional primary tumors, trained a machine learning classifier; the output predicted the presence and location of HNSCCUP for the six patients. Validation was performed using histopathology. RESULTS:Among the six HNSCCUP patients, p16+ occult primary was surgically identified in three patients, whereas three patients ultimately had no identifiable primary site in the oropharynx. FLIm correctly detected HNSCCUP in all three patients (ROC-AUC: 0.90 ± 0.06), and correctly predicted benign oropharyngeal tissue for the remaining three patients. The mean sensitivity was 95% ± 3.5%, and specificity 89% ± 12.7%. CONCLUSIONS:FLIm may be a useful diagnostic adjunct for detecting HNSCCUP.

CONTEXT.—:Pathology on-call experiences help prepare trainees for successful transition from residency to independent practice, and as such are an integral component of training. However, few data exist on anatomic pathology resident on-call workload and experience.OBJECTIVE.—:To obtain an overall picture of the anatomic pathology on-call experience to inform and improve resident education.DESIGN.—:Retrospective and prospective review of daily anatomic pathology on-call summaries from July 2016 to June 2020.RESULTS.—:During the first 2 years of the study (ie, retrospective portion), only 19% of on-call summaries (138 of 730) were available for review. After interventions, the on-call summary submission rate jumped to 98% (716 of 731). After-hours calls were most frequent on weekdays from 5 to 8 pm. The most frequent requests were for frozen sections (55%; 619 of 1125 calls), inquiries regarding disposition of fresh placentas (13%; 148 of 1125 calls), and inquiries regarding disposition of various other specimens (6%; 68 of 1125 calls). After-hours frozen section requests were most frequent for gynecologic and head and neck specimens. Notably, a significant number of after-hours calls were recurring preanalytic issues amenable to system-level improvements. We were able to eliminate the most common of these recurring preanalytic calls with stepwise interventions.CONCLUSIONS.—:To our knowledge, this is the first study analyzing the anatomic pathology resident on-call experience. In addition to obtaining a broad overview of the residents' clinical exposure on this service, we identified and resolved issues critical to optimal patient care (eg, inconsistent "patient hand-off") and improved the resident on-call experience (eg, fewer preanalytic calls increased resident time for other clinical, educational, or wellness activities).

The primary standard of care for Head and Neck (H&N) cancer patients is the complete surgical removal of cancer. Tissue classifiers based of autofluorescence lifetime imaging (FLIm) parameters have shown potential to differentiate healthy from cancer tissue in H&N patients and thus enhance the accuracy of this procedure. Here we report how collective autofluorescence trends (100-patient cohort, oral/oropharyngeal cancer) driving healthy vs. tumor contrast depend on anatomical location, patient medical history (e.g. tobacco use) and surgical context (in vivo vs. ex vivo). Accounting for such biological variables may further improve the accuracy of FLIm-guided H&N cancer surgery.

Cholangiocarcinoma (CCA) is a heterogenous group of malignancies originating in the biliary tree, and associated with poor prognosis. Until recently, treatment options have been limited to surgical resection, liver-directed therapies, and chemotherapy. Identification of actionable genomic alterations with biomarker testing has revolutionized the treatment paradigm for these patients. However, several challenges exist to the seamless adoption of precision medicine in patients with CCA, relating to a lack of awareness of the importance of biomarker testing, hurdles in tissue acquisition, and ineffective collaboration among the multidisciplinary team (MDT). To identify gaps in standard practices and define best practices, multidisciplinary hepatobiliary teams from the University of California (UC) Davis and UC Irvine were convened; discussions of the meeting, including optimal approaches to tissue acquisition for diagnosis and biomarker testing, communication among academic and community healthcare teams, and physician education regarding biomarker testing, are summarized in this review.