Accurate prognostic biomarkers in early-stage melanoma are urgently needed to stratify patients for clinical trials of adjuvant therapy. We applied a previously developed open source deep learning algorithm to detect tumor-infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) images of early-stage melanomas. We tested whether automated digital (TIL) analysis (ADTA) improved accuracy of prediction of disease specific survival (DSS) based on current pathology standards. ADTA was applied to a training cohort (n = 80) and a cutoff value was defined based on a Receiver Operating Curve. ADTA was then applied to a validation cohort (n = 145) and the previously determined cutoff value was used to stratify high and low risk patients, as demonstrated by Kaplan–Meier analysis (p ≤ 0.001). Multivariable Cox proportional hazards analysis was performed using ADTA, depth, and ulceration as co-variables and showed that ADTA contributed to DSS prediction (HR: 4.18, CI 1.51–11.58, p = 0.006). ADTA provides an effective and attainable assessment of TILs and should be further evaluated in larger studies for inclusion in staging algorithms.
SUMMARY The prefrontal cortex (PFC) regulates a wide range of sensory experiences. Chronic pain is known to impair normal neural response, leading to enhanced aversion. However, it remains unknown how nociceptive responses in the cortex are processed at the population level and whether such processes are disrupted by chronic pain. Using in vivo endoscopic calcium imaging, we identify increased population activity in response to noxious stimuli and stable patterns of functional connectivity among neurons in the prelimbic (PL) PFC from freely behaving rats. Inflammatory pain disrupts functional connectivity of PFC neurons and reduces the overall nociceptive response. Interestingly, ketamine, a well-known neuromodulator, restores the functional connectivity among PL-PFC neurons in the inflammatory pain model to produce anti-aversive effects. These results suggest a dynamic resource allocation mechanism in the prefrontal representations of pain and indicate that population activity in the PFC critically regulates pain and serves as an important therapeutic target.
Chronic pain alters cortical and subcortical plasticity, causing enhanced sensory and affective responses to peripheral nociceptive inputs. Previous studies have shown that ketamine had the potential to inhibit abnormally amplified affective responses of single neurons by suppressing hyperactivity in the anterior cingulate cortex (ACC). However, the mechanism of this enduring effect has yet to be understood at the network level. In this study, we recorded local field potentials from the ACC of freely moving rats. Animals were injected with complete Freund’s adjuvant (CFA) to induce persistent inflammatory pain. Mechanical stimulations were administered to the hind paw before and after CFA administration. We found a significant increase in the high-gamma band (60–100 Hz) power in response to evoked pain after CFA treatment. Ketamine, however, reduced the high-gamma band power in response to evoked pain in CFA-treated rats. In addition, ketamine had a sustained effect on the high-gamma band power lasting up to five days after a single dose administration. These results demonstrate that ketamine has the potential to alter maladaptive neural responses in the ACC induced by chronic pain.
1561 Background: Delayed diagnosis and care of mental health disorders (MHD) is a significant challenge in the care for patients with cancer. The objective of this study was to use natural language processing (NLP) to identify words related to mental health documented in clinical notes surrounding the time of cancer diagnosis and assess their predictive ability of future, new MHD. Methods: This single institution cohort study consisted of patients diagnosed with cancer between January 2012 and November 2022. Cancer and MHD were identified based on ICD-10 codes obtained from deidentified electronic health record data. MHD included psychotic disorders (F20-29), mood disorders (F30-39), and anxiety disorders (F40-48). The clinical Text Analysis Knowledge Extraction System was applied to deidentified clinical notes, and symptoms mapped to SNOMED concepts relevant to mental health were identified. These mental health symptoms were aggregated in the 15 days preceding and 15 days following a first cancer diagnosis and analyzed across MHD status. Patient characteristics including sex, age, race, cancer, and insurance were also analyzed. Results: This cohort consisted of 64,010 patients with cancer who had no documented MHD prior to cancer diagnosis, with a majority being 40-64 years old (45.8%) or 65+ (43.7%) and identifying as male (53.0%) or white (60.2%). Most patients had prostate (12.5%), hematologic (10.8%), or breast (10.3%) cancer and private insurance (46.2%). 9,825 (15.3%) patients developed a newly documented MHD, with a median time of 139 days (IQR: 40-466) from cancer diagnosis. The top five most common mental health documented symptoms for all patients were normal mood (23.3%), mental state finding (17.9%), worried (10.2%), feeling content (9.9%), and cognitive function finding (6.6%). Those who had a future MHD had higher documented rates across all mental health symptoms. Multivariate cox proportional hazards model identified 18-39 years old, female, white, and Medicaid or Medicare insurance as independent factors associated with an increased risk of a future, new MHD. Prostate cancer was associated with lower risk of a future MHD. Panic (OR 2.1 [95% CI 1.8-2.4]), feeling nervous (1.9 [1.5-2.4]), feeling guilt (1.9 [1.4-2.5]), mild anxiety (1.8 [1.4-2.4]), and feeling frustrated (1.4 [1.2-1.6]) were identified as the symptoms most strongly associated with an increased risk of a future MHD. Conclusions: NLP extracted mental health symptoms documented in clinical notes correlated with an increased risk of documented MHD. Computational approaches may be tools for improving the timely diagnosis of MHD and referral to specialty services. Further work is needed to investigate potential disparities in documentation and management of care for patients with cancer who develop MHD, including delays between documentation and eventual diagnosis.
10076 Background: Patients diagnosed with early stage melanoma are at risk of recurrence and death. Adjuvant therapy decreases risk but incurs toxicity and expense. While tumor-infiltrating lymphocytes (TILs) improve prognosis, studies have shown conflicting results due, at least in part, to inter-observer variability. Thus, TILs are not included in standard American Joint Committee on Cancer (AJCC) staging. Here, we quantitatively analyze TILs in hematoxylin and eosin (H&E) melanoma images using two machine learning algorithms. Methods: H&E images were evaluated by two methods for patients with resectable stage I-III melanoma from Columbia (N = 81) and validated using samples from Geisinger and Moffitt (N = 128). For both methods, H&E images were manually annotated using open source software, QuPath, to specify tumor regions. For Method A, images were divided into patches and, for each patch, a probability was generated to detect lymphocytes. Patches above a set threshold were considered to be “TIL positive”. Ratio of TIL positive patches to total patches was assessed for every image. For Method B, a classifier was manually trained in QuPath and then applied on each image to determine the ratio of the areas of all immune cells to all tumor cells as previously published. Cutoff values to define high and low risk groups were established based on a test set and then validated in an independent cohort. Results: Both methods distinguished patients with visceral recurrence from those without for the Columbia training set (Method A p = .0015, Method B p = .043). Using Method A, Kaplan-Meier curve at the selected cutoff also correlated significantly with disease specific survival (DSS) for Columbia (p = .022) and was validated in the Geisinger/Moffitt (p = .046) cohort. Cox analysis using Method A showed that TIL status predicted DSS in the validation set (p = .047) and added significantly to depth and ulceration (HR = 3.43, CI: 1.047-11.257, p = .042). Conclusions: Both open source machine learning algorithms find significantly higher TILs in patients who do not develop metastasis. Notably, Method A may add to standard predictors, such as depth and ulceration. These results demonstrate the promise of computational algorithms to enhance visual grading, and suggest that digital TIL evaluation may add to current AJCC staging. [Table: see text]
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