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Introduction Rapid detection of large vessel occlusion (LVO) is very crucial in triaging stroke patients potentially candidates for mechanical thrombectomy (MT). Hyperdense vessel sign (HDVS) is one of the earliest ischemic changes in non‐contrast CT scan (NCCT) indicating LVO stroke. Artificial intelligence emerged to detect HDVS with the advantages of faster acquisition, less variation, and a lower need for experience than the usual detection. We aimed to identify the diagnostic performance of automated software (e‐Stroke, Brainomix) in HDVS detection. Methods A prospectively collectedMT database from March 2020 to August 2021 was reviewed. Patients were included if they had intracranial internal carotid artery or middle cerebral artery M1 or M2 occlusion. Cases with HDVS were identified through the routine 2.5‐mm slice thickness NCCT scans after being correlated with patients’ clinical information and confirmed with CT angiography (CTA) scans. NCCT scans were classified according to slice thickness into two groups: 2.5‐mm scans and 0.625‐mm generated scans. All NCCT scans were read by e‐Stroke software, then deidentified and reviewed by two stroke neurologists who were blinded to any clinical, other imaging, or therapeutic information. They were required to record the presence/laterality of HDVS before and after observing other NCCT early ischemic changes like gaze deviation, loss of insular ribbon, caudate or lentiform hypodensity. ROC curve analysis was used to estimate sensitivity and specificity and the area under the curve (AUC) was compared using DeLong’s test. Inter‐rater agreement between the two readers’ final reads, e‐Stroke, and the standard read was measured using the Fleiss Kappa test. Results Among 304 patients included in the study, 37.7% had HDVS. Approximately 44% of the scans had 2.5‐mm slice thickness and 56% had 0.625‐mm slice thickness. The e‐Stroke software identified HDVS with a sensitivity of 63% and a specificity of 71% (Table 1). The mean AUC value of e‐Stroke HDVS detection (0.67[0.61‐0.74]) was similar to reader‐1 (0.68[0.62‐0.74];p = 0.87) and reader‐2 (0.63[0.57‐0.70];p = 0.56). HDVS detection improved by reader‐1(0.78[0.72‐0.83];p = 0.03) after observing other early ischemic changes on the same scans, but reader‐2 performance remained similar to e‐Stroke (0.69[0.63‐0.76];p = 0.71). AUC, sensitivity and specificity ofHDVS detection by e‐Stroke were significantly higher using 2.5‐mm compared to 0.625‐mm sliced NCCT scans (0.78[0.70‐0.86],sensitivity 70%,specificity 86%;p< 0.001) vs (0.58[0.50‐0.67],sensitivity 56%,specificity 61%;p = 0.06) respectively;p = 0.01. The readers also had higher AUC values with 2.5‐mm scans but not statistically significant, (0.74[0.66‐0.83] vs 0.64[0.56‐0.73];p = 0.18) for reader‐1 and (0.68[0.59‐0.77] vs 0.57[0.48‐0.66];p = 0.23) for reader‐2. The same after the final read, (0.85[0.78‐0.92] vs 0.75[0.67‐0.82];p = 0.08) for reader‐1 and (0.73[0.65‐0.82] vs 0.67[0.58‐0.76];p = 0.43) for reader‐2. Similarly, inter‐rater agreement was higher using 2.5‐mm sliced scans, k = 0.50(0.43‐0.75) compared to0.625‐mm scans,k = 0.27(0.21‐0.33). Conclusions Artificial intelligence (e‐Stroke software) has comparable sensitivity and specificity to human readers in HDVS detection. For e‐Stroke software, 2.5‐mm sliced CT scans are better to identifyHDVS compared to 0.625‐mm scans.

Introduction Carotid Web (CaW) represents an important and overlooked etiology for ischemic stroke and has been associated with high rates of stroke recurrence. Computed tomography angiography (CTA) has been shown to have comparable performance to digital subtraction angiography (DSA) and has been suggested to be the non‐invasive imaging of choice for CaW detection. However, misdiagnosis has been demonstrated to be common even in specialized centers. We evaluated the impact of adding CTA multiplane reconstruction (MPR) andthree‐dimensional maximum intensity projection (3D MIP) reformat on the diagnostic performance of CTA in CaW detection. Methods After exclusion of patients aged >65 years old and patients with no available/poor quality CTA,CaW cases (n = 31 consecutive patients leading to 31 ipsilateral carotids to the stroke derived from out prospective CaW database), as well as two other groups: 1)carotid atherosclerosis (n = 27consecutivepatients from out carotid stenting database leading to 27 carotids contralateral to the index lesion) and 2) consecutive normal carotid cases (n = 49 patients with normal carotids extracted from the electronic medical records for patients imaged due to suspected blunt cerebrovascular trauma) were included. All CTA images were deidentified and reviewed independently by three stroke neurologists to record the diagnosis and level of diagnostic certainty (in form of a scale (1[lowest]‐to‐5[highest]) after evaluating the CTA axial plane alone, then after sagittal and coronal planes (MPR) reconstructions, and then after evaluation of3D MIP reformatted images.The analyses were made for the total number of observations for all readers (93 CaWs, 81 atherosclerosis cases and 147 normal carotids). Results On reviewing CTA axial projection alone, raters correctly diagnosed 44.1% of CaW, 87.7% of carotid atherosclerosis and 83% of normal carotid images. Sagittal and coronal MPR significantly increased the rate of accurate CaW diagnosis (76.3%‐Table 1) The certainty level for CaW diagnosis was lower when compared to atherosclerosis as well as normal carotid using the CTA axial projection alone (3.0[3.0‐4.0] vs 4.0[3.0‐5.0];p< 0.001 and vs 4.0[3.0‐5.0];p< 0.001) as well as after adding sagittal/coronal MPR (4.0[3.0‐5.0] vs 5.0[4.0‐5.0],p = 0.01; and vs 4.0[4.0‐5.0],p< 0.001). The certainty level became similar between CaW and atherosclerosis as well as normal carotids with the addition of 3D MIP (5.0[5.0‐5.0] vs 5.0[4.5‐5.0], p = 0.61; and vs 5.0[5.0‐5.0],p = 0.15) respectively. Inter‐rater agreement in CaW detection increased from k = 0.46(0.35‐0.57);p< 0.05usingaxial section to k = 0.80(0.69‐0.91);p< 0.05 with MPR. Axial projection alone had lower sensitivity in CaW detection (AUC = 0.69(0.62‐0.76),sensitivity = 44%,specificity = 95%,p< 0.05) compared to MPR (AUC = 0.86(0.80‐0.91),sensitivity = 76%,specificity = 96%,p< 0.05). Misdiagnosed CaW cases, after using all planes with 3D MIP (n = 23/93), were older (56[46‐61] vs 52[46‐57] years,p = 0.04) and lower length/base ratio (0.51[0.49‐0.87] vs 0.92[0.74‐1.19],p< 0.001) compared to the correctly diagnosed CaW cases (n = 70/93). Conclusions CTA axial plane alone is unreliable to detect CaW and the addition of sagittal/coronal MPR and 3D MIPs are important to increase accurate diagnosis and perceived reader diagnostic certainty.

AbstractOral cancer is one of the leading causes of death worldwide. Oral precancerous lesions (OPL) are the precursors of oral cancer, with varying degrees of progression. Tetrahydrocurcumin (THC) is a major metabolite of curcumin with superior anticancer properties against various types of cancer. However, THC’s clinical outcome is limited by its poor aqueous solubility. Herein, we developed novel mucoadhesive biopolymer-based composite sponges for buccal delivery of THC, exploiting nanotechnology and mucoadhesion for efficient prevention and treatment of oral cancer. Firstly, THC-nanocrystals (THC-NC) were formulated and characterized for subsequent loading into mucoadhesive composite sponges. The anticancer activity of THC-NC was assessed on a human tongue squamous carcinoma cell line (SCC-4). Finally, the chemopreventive activity of THC-NC loaded sponges (THC-NC-S) was examined in DMBA-induced hamster OPL. The selected THC-NC exhibited a particle size of 532.68 ± 13.20 nm and a zeta potential of −46.08 ± 1.12 mV. Moreover, THC-NC enhanced the anticancer effect against SCC-4 with an IC50 value of 80 µg/mL. THC-NC-S exhibited good mucoadhesion properties (0.24 ± 0.02 N) with sustained drug release, where 90% of THC was released over 4 days. Furthermore, THC-NC-S had a magnificent potential for maintaining high chemopreventive activity, as demonstrated by significant regression in the dysplasia degree and a decline in cyclin D1 (control: 40.4 ± 12.5, THC-NC-S: 12.07 ± 5.2), culminating in significant amelioration after 25 days of treatment. Conclusively, novel THC-NC-S represent a promising platform for local therapy of OPL, preventing their malignant transformation into cancer.