Hypopharyngeal squamous cell carcinoma (HPSCC) has a poor prognosis due, in large part, to distant metastasis (DM). Although induction therapy (IT) can reduce DM rates, its translation to an overall survival (OS) benefit remains unclear, highlighting the need for tools to identify patients who would benefit most from IT. This study developed a nomogram to predict the risk for DM in patients with HPSCC, and assessed the survival benefit of IT across risk groups. Data from patients obtained from the Surveillance, Epidemiology, and End Results (i.e., "SEER") database (2004-2015) were randomly assigned to 1 of 2 groups at a ratio of 7:3: training; and internal validation. The external validation set comprised patients from 2 medical centers in China. Risk factors were identified using multivariate logistic regression analysis. Male sex, T classification ≥ 2, N classification ≥ 1, and poorer histological grade were independent risk factors for DM. The nomogram demonstrated good discriminative ability, with areas under the receiver operating characteristic curve of 0.702 (95% confidence interval [CI]: 0.669-0.735) in the training set, 0.704 (95% CI: 0.648-0.759) in the internal validation set, and 0.863 (95% CI: 0.804-0.923) in the external validation set. Based on the optimal cut-off value, patients were stratified into high- and low-risk groups. In the high-risk group, patients who received IT exhibited significantly improved OS (hazard ratio [HR] 0.364; 95% CI: 0.165-0.805; P = 0.040) and progression-free survival (PFS; HR 0.420; 95% CI: 0.190-0.928; P = 0.042) compared with those who did not receive IT. There was no significant survival benefit from IT in the low-risk group (OS: HR 0.881; 95% CI: 0.414-1.875, P = 0.095; PFS: HR 0.544 95% CI: 0.182-1.626, P = 0.250). This study constructed and preliminarily validated a nomogram for predicting DM in patients with HPSCC, and may serve as an exploratory tool for screening high-risk patients who are likely to benefit from IT, thereby providing a reference for the design of future prospective intervention trials.

SLC16A3, belonging to the SLC16 gene family, is involved in the transportation of monocarboxylate. SLC16A family members play important roles in tumorigenesis, nonetheless, the specific involvement of SLC16A3 in tumor prognosis and diagnosis in human cancers remains unelucidated. This study dealt with the exploration of SLC16A3 expression in human pan-cancer and its significance regarding disease prognosis. For this investigation, the mRNA expression data of SLC16A3 were acquired from the TCGA and the GTEx datasets. The Kaplan-Meier plots, univariate Cox regression, and the ROC curve were employed for assessing the prognostic and diagnostic significance of SLC16A3 in pan-cancer. Furthermore, the cBioPortal database was used to analyze the SLC16A3 genomic alterations. Moreover, the association of the infiltration of immune cells and immune checkpoint genes with SLC16A3 was analyzed by the TIMER database. Gene Ontology and KEGG pathway analysis were employed to explore the function of SLC16A3 in pan-cancer. The resulting data demonstrated that SLC16A3 mRNA expression was overexpressed in most cancers and its protein expression was also high across diverse cancer types. Moreover, upregulated SLC16A3 expression was linked to poor OS and PFI of certain cancers. Cox regression analysis further indicated that SLC16A3 is a risk factor for patients with PAAD, CESC, LUSC, LUAD, CHOL, LGG, MESO, and OSCC. The ROC curve revealed that SLC16A3 exhibited a high accuracy (AUC > 0.9) in BRCA, CHOL, ESCA, GBM, and KIRC prediction. Moreover, the acquired data indicated that in pan-cancer, the SLC16A3 expression exhibited correlations with immune checkpoint genes and immune cells. These findings collectively suggest that SLC16A3 holds promise as a biomarker for diagnostic and prognostic purposes in pan-cancer.

Purpose Among all primary breast tumors, malignant phyllodes tumor of the breast (MPTB) make up less than 1%. In the treatment of phyllode tumors, surgical procedures such as mastectomy and breast-conserving surgery are the mainstay. MPTB has, however, been controversial when it comes to treating it with RT. We aimed to explore the prognostic impact of RT and other clinicopathologic factors on long-term survival for patients with stage T3 or T4 malignant phyllodes tumors. Methods We select patients with stage T3 or T4 MPTB who qualified for the criteria between 2000 and 2018 via the Surveillance, Epidemiology, and End Results (SEER) database. We performed 1:1 propensity score matching (PSM) and Kaplan–Meier analysis to explore the role of RT in long-term survival of patients with stage T3 or T4 MPTB. A univariate and multivariate analysis of breast cancer-specific survival (BCSS) and overall survival (OS) risk factors was carried out using a Cox proportional hazards model. In addition, the nomogram graph of OS and BCSS was constructed. Results A total of 583 patients with stage T3 or T4 malignant phyllodes tumors were included in this study, of whom 154 (26.4%) received RT, and 429 (73.6%) were treated without RT. Before adjustment, between groups with and without RT, BCSS ( p = 0.1) and OS ( p = 0.212) indicated no significant difference respectively. Using of PSM, the two groups still did not differ significantly in BCSS ( p = 0.552) and OS ( p = 0.172). In multivariate analysis, age ( p < 0.001), surgery of primary site ( p < 0.001) and distant metastatic status ( p < 0.001) were related to prognosis, while RT still did not affect BCSS ( p = 0.877) and OS ( p = 0.554). Conclusion Based on the SEER database analysis, the study suggests that the patients with stage T3 or T4 MPTB treated with RT after surgery didn't have significant differences in BCSS or OS compared to those not treated with RT.

Infrared small maritime target detection under strong ocean waves, a challenging task, plays a key role in maritime distress target search and rescue applications. Many methods based on directionality or gradient properties have proven to perform well for infrared images with heterogeneous scenarios. However, they tend to perform poorly when facing strong ocean wave background, mainly due to the following: 1) infrared images have low signal-to-clutter ratio with low intensity for small targets; 2) some waves have high local contrast that may be similar to or higher than targets. To solve these issues, a new method based on gradient vector field characterization (GVFC) of infrared images is proposed. First, we construct the gradient vector field and coarsely extract suspected targets. Then, gradient vector distribution measure (GVDM) is presented, which comprehensively integrates a synergistic homogeneity test based on Kolmogorov–Smirnov test with absolute difference standard deviation for gradient direction angle and regression analysis for gradient modulus. The proposed GVDM takes advantage of pixel-level gradient distribution property to further filtrate refined suspected targets. Moreover, gradient modulus horizontal local dissimilarity is proposed to measure the diversity of gradient modulus in horizontal direction between targets and waves, so as to enhance target saliency and suppress residual clutter simultaneously, which achieves preferable performance. Finally, a simple adaptive threshold is applied to confirm targets. Extensive experiments implemented on infrared maritime images with strong ocean waves demonstrate that the proposed method is superior to the state-of-the-art methods with respect to robustness and detection accuracy.

The most dangerous variety of glioma, glioblastoma, has a high incidence and fatality rate. The prognosis for patients is still bleak despite numerous improvements in treatment approaches. We urgently need to develop clinical parameters that can evaluate patients' conditions and predict their prognosis. Various parameters are available to assess the patient's preoperative performance status and degree of frailty, but most of these parameters are subjective and therefore subject to interobserver variability. Sarcopenia can be used as an objective metric to measure a patient's physical status because studies have shown that it is linked to a bad prognosis in those with cancers. For the purpose of identifying sarcopenia, temporal muscle thickness has demonstrated to be a reliable alternative for a marker of skeletal muscle content. As a result, patients with glioblastoma may use temporal muscle thickness as a potential marker to correlate with the course and fate of their disease. This narrative review highlights and defines the viability of using temporal muscle thickness as an independent predictor of survival in glioblastoma patients, and it evaluates recent research findings on the association between temporal muscle thickness and prognosis of glioblastoma patients.

PurposeTo evaluate the feasibility of using a simplified non-coplanar volumetric modulated arc therapy (NC-VMAT) and investigate its dosimetric advantages compared with intensity modulated radiation therapy (IMRT) and coplanar volumetric modulated arc therapy (C-VMAT) for hippocampal-avoidance whole brain radiation therapy (HA-WBRT).MethodsTen patients with brain metastase (BM) were included for HA-WBRT. Three treatment plans were generated for each case using IMRT, C-VMAT, and NC-VMAT, respectively.ResultsThe dosimetric results of the three techniques complied roughly with the RTOG 0933 criteria. After dose normalization, the V30Gy of whole brain planned target volume (WB-PTV) in all the plans was controlled at 95%. Homogeneity index (HI) of WB-PTV was significantly reduced in NC-VMAT (0.249 ± 0.017) over IMRT (0.265 ± 0.020, p=0.005) and C-VMAT (0.261 ± 0.014, p=0.020). In terms of conformity index (CI), NC-VMAT could provide a value of 0.821 ± 0.010, which was significantly superior to IMRT (0.788 ± 0.019, p<0.001). According to D2% of WB-PTV, NC-VMAT could provide a value of 35.62 ± 0.37Gy, significantly superior to IMRT (36.43 ± 0.65Gy, p<0.001). According to D50% of WB-PTV, NC-VMAT can achieve the lowest value of 33.18 ± 0.29Gy, significantly different from IMRT (33.47 ± 0.43, p=0.034) and C-VMAT (33.58 ± 0.37, p=0.006). Regarding D2%, D98%, and Dmean of hippocampus, NC-VMAT could control them at 15.57 ± 0.18Gy, 8.37 ± 0.26Gy and 11.71 ± 0.48Gy, respectively. D2% and Dmean of hippocampus for NC-VMAT was significantly lower than IMRT (D2%: 16.07 ± 0.29Gy, p=0.001 Dmean: 12.18 ± 0.33Gy, p<0.001) and C-VMAT (D2%: 15.92 ± 0.37Gy, p=0.009 Dmean: 12.21 ± 0.54Gy, p<0.001). For other organs-at-risk (OARs), according to D2% of the right optic nerves and the right lenses, NC-VMAT had the lowest values of 31.86 ± 1.11Gy and 7.15 ± 0.31Gy, respectively, which were statistically different from the other two techniques. For other organs including eyes and optic chiasm, NC-VMAT could achieve the lowest doses, different from IMRT statistically.ConclusionThe dosimetry of the three techniques for HA-WBRT could roughly comply with the proposals from RTOG 0933. After dose normalization (D95%=30Gy), NC-VMAT could significantly improve dose homogeneity and reduce the D50% in the brain. Besides, it can reduce the D2% of the hippocampus, optic nerves, and lens. With this approach, an efficient and straightforward plan was accomplished.

Infrared target detection is a key technology in maritime distress target search and tracking systems. Particularly, detecting small targets overwhelmed in heavy waves is an important and challenging task. In order to effectively enhance small infrared maritime target saliency and suppress heavy wave clutter, a small infrared maritime target detection method based on gradient amplitude difference and multidimensional dissimilarity measure is proposed in this paper. Firstly, we employ Sobel operator to measure the gradient amplitude difference (GAD) by calculating minimum component of gradient between horizontal and vertical directions. Meanwhile, we use facet kernel filtering followed by adaptive threshold segmentation to extract the sizes, shapes, and locations of candidate targets; then, multidimensional dissimilarity information, i.e. multi-direction and multi-scale, is constructed based on original infrared image and locations of candidate targets. Multidimensional dissimilarity measure (MDM) achieves target enhancement and background clutter suppression. The final saliency map is obtained by multiplying GAD and MDM. Finally, an adaptive threshold is used to segment targets from residual interferences. Experimental results on three real infrared maritime image sequences show that, the proposed method achieves better performance in terms of local contrast gain, background suppression factor, and detection probability with low false alarm. Our method performs more satisfactorily and robustly than the state-of-the-art methods.

When detecting diverse infrared (IR) small maritime targets on complicated scenes, the existing methods get into trouble and unsatisfactory performance. The main reasons are as follows: 1) affected by target characteristics and ambient temperature and so on, both bright and dark targets may exist on IR maritime images in practical application and 2) spatial information and temporal correlation of targets are not fully excavated. To these problems, we propose a robust anti-jitter spatial–temporal trajectory consistency (ASTTC) method, the main idea of which is to improve detection accuracy using single-frame detection followed by multi-frame decision. First, we innovatively design adaptive local gradient variation (ALGV) descriptor, which combines local dissimilarity measure with gradient magnitude distribution to enhance the local contrast for both bright and dark targets so that the suspected targets can be robustly extracted. For multi-frame decision, interframe displacement correction is necessary to eliminate the interference of IR imager motion and vibration for target codeword. We use pyramid optical flow to track feature point extracted by Shi-Tomasi to capture interframe registration coefficients. Then, the target position is corrected in the spatial–temporal domain. Finally, a robust spatial–temporal trajectory descriptor (STTD), which achieves target encoding and target trajectory consistency measurement, is designed to further confirm real targets and eliminate false targets. Experiments conducted on various real IR maritime image sequences demonstrate the applicability and robustness of ASTTC, which performs more satisfactorily than the existing methods in detection accuracy.

Efficient small infrared (IR) maritime target detection in heavy waves is a key and challenging task in maritime distress target search and rescue systems. The current methods are struggling to enhance targets and suppress heavy waves because of the similar local contrast. In this letter, a weighted multidirectional gradient (WMDG) measure is presented. First, the candidate targets are extracted via facet model. Then multidirectional gradient and difference information are constructed to calculate average cumulative multidirectional gradient (ACMG) and achieve directional difference measure (DDM). Consequently, the final saliency is reconstructed via ACMG weighted by DDM. Finally, the actual targets are segmented through an adaptive threshold. Experimental results demonstrate that our method is superior to the state-of-the-art methods with regard to detection validity and robustness for small IR maritime target detection in heavy waves.

Robust and effective detection of a small target in an infrared maritime image is a key technology of maritime target search and tracking applications. Infrared small target detection is a challenging task due to the factors such as dim small targets and various complex backgrounds caused by sun glitters and strong waves. In this article, the integrated target saliency measure (ITSM) based on local and nonlocal spatial information is proposed to improve target detection performance. We combine local heterogeneity property of targets and nonlocal self-correlation property of background with targets’ sparsity to separate real targets from background clutters. First, local heterogeneity calculation based on cross-window standard deviation (CSD) is proposed to extract candidate targets preliminarily, which enhances the local intensity difference between small targets and neighboring background. Meanwhile, low-rank representation (LRR) is applied to background prediction and removal, which is followed by adaptive threshold segmentation to enhance target saliency. Finally, we integrate the results obtained from the two steps mentioned above to further enhance targets and suppress background clutters. Then, real targets are extracted by an iterative threshold on the integrated map so as to generate the seed map, in addition, the target expansion strategy is exploited to keep full target areas. Experimental results on six datasets show that the proposed method outperforms the state-of-the-art methods in terms of robustness and target detection accuracy.

In the use of airborne optoelectronic pod to track sea surface distress targets in harsh sea conditions, the sudden vibration or steering of a rescue helicopter can easily result in the loss of a target. In order to solve the problem of target loss in the process of target tracking, a fast algorithm of sea surface target retrieval based on spatial location information is proposed in this paper. The key idea of the algorithm is to use the method of coordinate transformation to transform the spatial location information corresponding to the previous frame of the target lost and the corresponding spatial location information of the current infrared camera into two corresponding coordinates under the same global coordinate system. According to the coordinates of the two spatial locations, calculate the angle between the camera and the target, and then adjust the angle of the fiber optic gyroscopes camera so that the target quickly return to the image field of view. The algorithm is validated by the ground experiment and the flight experiment, which has practical engineering significance for the rapid search and rescue of the sea surface distress target.