Predicting central cervical lymph node metastasis in papillary thyroid carcinoma with Hashimoto's thyroiditis: a practical nomogram based on retrospective study.

Background: In papillary thyroid carcinoma (PTC) patients with Hashimoto's thyroiditis (HT), preoperative ultrasonography frequently reveals the presence of enlarged lymph nodes in the central neck region. These nodes pose a diagnostic challenge due to their potential resemblance to metastatic lymph nodes, thereby impacting the surgical decision-making process for clinicians in terms of determining the appropriate surgical extent. Methods: Logistic regression analysis was conducted to identify independent risk factors associated with central lymph node metastasis (CLNM) in PTC patients with HT. Then a prediction model was developed and visualized using a nomogram. The stability of the model was assessed using ten-fold cross-validation. The performance of the model was further evaluated through the use of ROC curve, calibration curve, and decision curve analysis. Results: A total of 376 HT PTC patients were included in this study, comprising 162 patients with CLNM and 214 patients without CLNM. The results of the multivariate logistic regression analysis revealed that age, Tg-Ab level, tumor size, punctate echogenic foci, and blood flow grade were identified as independent risk factors associated with the development of CLNM in HT PTC. The area under the curve (AUC) of this model was 0.76 (95% CI [0.71-0.80]). The sensitivity, specificity, accuracy, and positive predictive value of the model were determined to be 88%, 51%, 67%, and 57%, respectively. Conclusions: The proposed clinic-ultrasound-based nomogram in this study demonstrated a favorable performance in predicting CLNM in HT PTCs. This predictive tool has the potential to assist clinicians in making well-informed decisions regarding the appropriate extent of surgical intervention for patients.

Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images.

Objectives: Auto-segmentation promises greater speed and lower inter-reader variability than manual segmentations in radiation oncology clinical practice. This study aims to implement and evaluate the accuracy of the auto-segmentation algorithm, "Masked Image modeling using the vision Transformers (SMIT)," for neck nodal metastases on longitudinal T2-weighted (T2w) MR images in oropharyngeal squamous cell carcinoma (OPSCC) patients. Methods: This prospective clinical trial study included 123 human papillomaviruses (HPV-positive [+]) related OSPCC patients who received concurrent chemoradiotherapy. T2w MR images were acquired on 3 T at pre-treatment (Tx), week 0, and intra-Tx weeks (1-3). Manual delineations of metastatic neck nodes from 123 OPSCC patients were used for the SMIT auto-segmentation, and total tumor volumes were calculated. Standard statistical analyses compared contour volumes from SMIT vs manual segmentation (Wilcoxon signed-rank test [WSRT]), and Spearman's rank correlation coefficients (ρ) were computed. Segmentation accuracy was evaluated on the test data set using the dice similarity coefficient (DSC) metric value. P-values <0.05 were considered significant. Results: No significant difference in manual and SMIT delineated tumor volume at pre-Tx (8.68 ± 7.15 vs 8.38 ± 7.01 cm3, P = 0.26 [WSRT]), and the Bland-Altman method established the limits of agreement as -1.71 to 2.31 cm3, with a mean difference of 0.30 cm3. SMIT model and manually delineated tumor volume estimates were highly correlated (ρ = 0.84-0.96, P < 0.001). The mean DSC metric values were 0.86, 0.85, 0.77, and 0.79 at the pre-Tx and intra-Tx weeks (1-3), respectively. Conclusions: The SMIT algorithm provides sufficient segmentation accuracy for oncological applications in HPV+ OPSCC. Advances in knowledge: First evaluation of auto-segmentation with SMIT using longitudinal T2w MRI in HPV+ OPSCC.

Artificial intelligence-based automated segmentation and radiotherapy dose mapping for thoracic normal tissues.

Background and purpose: Objective assessment of delivered radiotherapy (RT) to thoracic organs requires fast and accurate deformable dose mapping. The aim of this study was to implement and evaluate an artificial intelligence (AI) deformable image registration (DIR) and organ segmentation-based AI dose mapping (AIDA) applied to the esophagus and the heart. Materials and methods: AIDA metrics were calculated for 72 locally advanced non-small cell lung cancer patients treated with concurrent chemo-RT to 60 Gy in 2 Gy fractions in an automated pipeline. The pipeline steps were: (i) automated rigid alignment and cropping of planning CT to week 1 and week 2 cone-beam CT (CBCT) field-of-views, (ii) AI segmentation on CBCTs, and (iii) AI-DIR-based dose mapping to compute dose metrics. AIDA dose metrics were compared to the planned dose and manual contour dose mapping (manual DA). Results: AIDA required âˆ¼2 min/patient. Esophagus and heart segmentations were generated with a mean Dice similarity coefficient (DSC) of 0.80±0.15 and 0.94±0.05, a Hausdorff distance at 95th percentile (HD95) of 3.9±3.4 mm and 14.1±8.3 mm, respectively. AIDA heart dose was significantly lower than the planned heart dose (p = 0.04). Larger dose deviations (>=1Gy) were more frequently observed between AIDA and the planned dose (N = 26) than with manual DA (N = 6). Conclusions: Rapid estimation of RT dose to thoracic tissues from CBCT is feasible with AIDA. AIDA-derived metrics and segmentations were similar to manual DA, thus motivating the use of AIDA for RT applications.

Advancing the frontiers of colorectal cancer treatment: harnessing ferroptosis regulation.

In recent years, colorectal cancer incidence and mortality have increased significantly due to poor lifestyle choices. Despite the development of various treatments, their effectiveness against advanced/metastatic colorectal cancer remains unsatisfactory due to drug resistance. However, ferroptosis, a novel iron-dependent cell death process induced by lipid peroxidation and elevated reactive oxygen species (ROS) levels along with reduced activity of the glutathione peroxidase 4 (GPX4) antioxidant enzyme system, shows promise as a therapeutic target for colorectal cancer. This review aims to delve into the regulatory mechanisms of ferroptosis in colorectal cancer, providing valuable insights into potential therapeutic approaches. By targeting ferroptosis, new avenues can be explored for innovative therapies to combat colorectal cancer more effectively. In addition, understanding the molecular pathways involved in ferroptosis may help identify biomarkers for prognosis and treatment response, paving the way for personalized medicine approaches. Furthermore, exploring the interplay between ferroptosis and other cellular processes can uncover combination therapies that enhance treatment efficacy. Investigating the tumor microenvironment's role in regulating ferroptosis may offer strategies to sensitize cancer cells to cell death induction, leading to improved outcomes. Overall, ferroptosis presents a promising avenue for advancing the treatment of colorectal cancer and improving patient outcomes.

Wasserstein HOG: Local Directionality Extraction via Optimal Transport.

Directionally sensitive radiomic features including the histogram of oriented gradient (HOG) have been shown to provide objective and quantitative measures for predicting disease outcomes in multiple cancers. However, radiomic features are sensitive to imaging variabilities including acquisition differences, imaging artifacts and noise, making them impractical for using in the clinic to inform patient care. We treat the problem of extracting robust local directionality features by mapping via optimal transport a given local image patch to an iso-intense patch of its mean. We decompose the transport map into sub-work costs each transporting in different directions. To test our approach, we evaluated the ability of the proposed approach to quantify tumor heterogeneity from magnetic resonance imaging (MRI) scans of brain glioblastoma multiforme, computed tomography (CT) scans of head and neck squamous cell carcinoma as well as longitudinal CT scans in lung cancer patients treated with immunotherapy. By considering the entropy difference of the extracted local directionality within tumor regions, we found that patients with higher entropy in their images, had significantly worse overall survival for all three datasets, which indicates that tumors that have images exhibiting flows in many directions may be more malignant. This may seem to reflect high tumor histologic grade or disorganization. Furthermore, by comparing the changes in entropy longitudinally using two imaging time points, we found patients with reduction in entropy from baseline CT are associated with longer overall survival (hazard ratio = 1.95, 95% confidence interval of 1.4-2.8, p = 1.65e-5). The proposed method provides a robust, training free approach to quantify the local directionality contained in images.

Prostate cancer genetic risk and associated aggressive disease in men of African ancestry.

African ancestry is a significant risk factor for prostate cancer and advanced disease. Yet, genetic studies have largely been conducted outside the context of Sub-Saharan Africa, identifying 278 common risk variants contributing to a multiethnic polygenic risk score, with rare variants focused on a panel of roughly 20 pathogenic genes. Based on this knowledge, we are unable to determine polygenic risk or differentiate prostate cancer status interrogating whole genome data for 113 Black South African men. To further assess for potentially functional common and rare variant associations, here we interrogate 247,780 exomic variants for 798 Black South African men using a case versus control or aggressive versus non-aggressive study design. Notable genes of interest include HCP5, RFX6 and H3C1 for risk, and MKI67 and KLF5 for aggressive disease. Our study highlights the need for further inclusion across the African diaspora to establish African-relevant risk models aimed at reducing prostate cancer health disparities.

Wogonin attenuates inflammation and oxidative stress in lipopolysaccharide-induced mastitis by inhibiting Akt/NF-κB pathway and activating the Nrf2/HO-1 signaling.

Mastitis is a disease involved in inflammation of breast which affects human and animals. Wogonin is one bioactive compound from many Chinese herbal medicines, which have multiple properties, including anti-inflammatory activity. However, the roles of wogonin in mastitis progression are largely undefined. Mastitis models were established using LPS-treated mice and mammary epithelial cells (MECs). Infiltration of inflammatory cells was analyzed by hematoxylin-eosin staining and myeloperoxidase (MPO) activity. Inflammatory cytokine (TNF-α and IL-1ß) levels were detected via ELISA. The phosphorylation and total of Akt and NF-κB levels and content of Nrf2 and HO-1 were measured via western blot. Cell viability was examined by CCK-8 assay. Oxidative stress was assessed by ROS generation and levels of MDA, GSH, and SOD. Wogonin attenuated LPS-induced infiltration of inflammatory cells, increase of MPO activity and levels of TNF-α and IL-1ß, and activation of the Akt/NF-κB pathway in murine mammary gland tissues, and promoted activation of Nrf2/HO-1 signaling. Wogonin did not affect MEC viability, but mitigated LPS-induced inflammation in MECs by reducing TNF-α and IL-1ß levels. Wogonin relieved LPS-induced oxidative stress in MECs through decreasing ROS generation and MDA level and increasing GSH and SOD levels. Wogonin repressed LPS-induced activation of the Akt/NF-κB pathway in MECs and increased Nrf2/HO-1 signaling activation. Activated Akt/NF-κB signaling or Nrf2/HO-1 signaling inactivation reversed the suppressive effects of wogonin on LPS-induced inflammation and oxidative stress in MECs. Wogonin mitigates LPS-induced inflammation and oxidative stress of MECs via suppressing activation of the Akt/NF-κB signaling and activating Nrf2/HO-1 pathway, indicating the therapeutic potential of wogonin in mastitis.

Prenatal ultrasound diagnosis of congenital infantile fibrosarcoma and congenital hemangioma: Three case reports.

BACKGROUND: Congenital infantile fibrosarcoma (CIF) and congenital hemangioma (CH) have similarities on prenatal ultrasound and are rare. CASE SUMMARY: We report 3 cases of fetuses with superficial hypervascular tumors, confirmed by postnatal pathology as CIF (1 case) and CH (2 cases, including 1 in a twin fetus). In Case 1, a mass with a rich blood supply in the fetal axilla was discovered by prenatal ultrasound at 28+0 wk of gestation. The postpartum pathological diagnosis was CIF, the mass was surgically removed, and the prognosis of the child was good. In Case 2, at 23+1 wk of gestation, a mass was discovered at the base of the fetus's thigh on prenatal ultrasound. The postpartum pathological diagnosis was CH. After conservative treatment, the mass shrank significantly. Case 3 occurred in a twin fetus. At 30+0 wk of gestation, prenatal ultrasound revealed a bulging mass with a rich blood supply on the abdominal wall of one of the fetuses. Three weeks later, the affected fetus died, and the unaffected baby was successfully delivered by emergency cesarean section. The affected fetus was pathologically diagnosed with CH. CONCLUSION: Prenatal ultrasound can provide accurate information, such as the location, size and blood supply of a surface mass in a fetus. We found similarities between CIF and CH in prenatal ultrasound findings. Although it is difficult to distinguish these conditions by prenatal ultrasound alone, for superficial hypervascular tumors that rapidly increase in size in a short period, close ultrasound monitoring of the fetus is required to quickly address possible adverse outcomes.

ANO7 African-ancestral genomic diversity and advanced prostate cancer.

BACKGROUND: Prostate cancer (PCa) is a significant health burden for African men, with mortality rates more than double global averages. The prostate specific Anoctamin 7 (ANO7) gene linked with poor patient outcomes has recently been identified as the target for an African-specific protein-truncating PCa-risk allele. METHODS: Here we determined the role of ANO7 in a study of 889 men from southern Africa, leveraging exomic genotyping array PCa case-control data (n = 780, 17 ANO7 alleles) and deep sequenced whole genome data for germline and tumour ANO7 interrogation (n = 109), while providing clinicopathologically matched European-derived sequence data comparative analyses (n = 57). Associated predicted deleterious variants (PDVs) were further assessed for impact using computational protein structure analysis. RESULTS: Notably rare in European patients, we found the common African PDV p.Ile740Leu (rs74804606) to be associated with PCa risk in our case-control analysis (Wilcoxon rank-sum test, false discovery rate/FDR = 0.03), while sequencing revealed co-occurrence with the recently reported African-specific deleterious risk variant p.Ser914* (rs60985508). Additional findings included a novel protein-truncating African-specific frameshift variant p.Asp789Leu, African-relevant PDVs associated with altered protein structure at Ca2+ binding sites, early-onset PCa associated with PDVs and germline structural variants in Africans (Linear regression models, -6.42 years, 95% CI = -10.68 to -2.16, P-value = 0.003) and ANO7 as an inter-chromosomal PCa-related gene fusion partner in African derived tumours. CONCLUSIONS: Here we provide not only validation for ANO7 as an African-relevant protein-altering PCa-risk locus, but additional evidence for a role of inherited and acquired ANO7 variance in the observed phenotypic heterogeneity and African-ancestral health disparity.

Complex Transformer Network for Single-Angle Plane-Wave Imaging.

OBJECTIVE: Plane-wave imaging (PWI) is a high-frame-rate imaging technique that sacrifices image quality. Deep learning can potentially enhance plane-wave image quality, but processing complex in-phase and quadrature (IQ) data and suppressing incoherent signals pose challenges. To address these challenges, we present a complex transformer network (CTN) that integrates complex convolution and complex self-attention (CSA) modules. METHODS: The CTN operates in a four-step process: delaying complex IQ data from a 0° single-angle plane wave for each pixel as CTN input data; extracting reconstruction features with a complex convolution layer; suppressing irrelevant features derived from incoherent signals with two CSA modules; and forming output images with another complex convolution layer. The training labels are generated by minimum variance (MV). RESULTS: Simulation, phantom and in vivo experiments revealed that CTN produced comparable- or even higher-quality images than MV, but with much shorter computation time. Evaluation metrics included contrast ratio, contrast-to-noise ratio, generalized contrast-to-noise ratio and lateral and axial full width at half-maximum and were -11.59 dB, 1.16, 0.68, 278 µm and 329 µm for simulation, respectively, and 9.87 dB, 0.96, 0.62, 357 µm and 305 µm for the phantom experiment, respectively. In vivo experiments further indicated that CTN could significantly improve details that were previously vague or even invisible in DAS and MV images. And after being accelerated by GPU, the CTN runtime (76.03 ms) was comparable to that of delay-and-sum (DAS, 61.24 ms). CONCLUSION: The proposed CTN significantly improved the image contrast, resolution and some unclear details by the MV beamformer, making it an efficient tool for high-frame-rate imaging.

Alterations in the Epigenetic Machinery Associated with Prostate Cancer Health Disparities.

Prostate cancer is driven by acquired genetic alterations, including those impacting the epigenetic machinery. With African ancestry as a significant risk factor for aggressive disease, we hypothesize that dysregulation among the roughly 656 epigenetic genes may contribute to prostate cancer health disparities. Investigating prostate tumor genomic data from 109 men of southern African and 56 men of European Australian ancestry, we found that African-derived tumors present with a longer tail of epigenetic driver gene candidates (72 versus 10). Biased towards African-specific drivers (63 versus 9 shared), many are novel to prostate cancer (18/63), including several putative therapeutic targets (CHD7, DPF3, POLR1B, SETD1B, UBTF, and VPS72). Through clustering of all variant types and copy number alterations, we describe two epigenetic PCa taxonomies capable of differentiating patients by ancestry and predicted clinical outcomes. We identified the top genes in African- and European-derived tumors representing a multifunctional "generic machinery", the alteration of which may be instrumental in epigenetic dysregulation and prostate tumorigenesis. In conclusion, numerous somatic alterations in the epigenetic machinery drive prostate carcinogenesis, but African-derived tumors appear to achieve this state with greater diversity among such alterations. The greater novelty observed in African-derived tumors illustrates the significant clinical benefit to be derived from a much needed African-tailored approach to prostate cancer healthcare aimed at reducing prostate cancer health disparities.

A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients.

OBJECTIVE: Based on ultrasound (US) images, this study aimed to detect and quantify calcifications of thyroid nodules, which are regarded as one of the most important features in US diagnosis of thyroid cancer, and to further investigate the value of US calcifications in predicting the risk of lymph node metastasis (LNM) in papillary thyroid cancer (PTC). METHODS: Based on the DeepLabv3+ networks, 2992 thyroid nodules in US images were used to train a model to detect thyroid nodules, of which 998 were used to train a model to detect and quantify calcifications. A total of 225 and 146 thyroid nodules obtained from two centers, respectively, were used to test the performance of these models. A logistic regression method was used to construct the predictive models for LNM in PTCs. RESULTS: Calcifications detected by the network model and experienced radiologists had an agreement degree of above 90%. The novel quantitative parameters of US calcification defined in this study showed a significant difference between PTC patients with and without cervical LNM (p < 0.05). The calcification parameters were beneficial to predicting the LNM risk in PTC patients. The LNM prediction model using these calcification parameters combined with patient age and other US nodular features showed a higher specificity and accuracy than the calcification parameters alone. CONCLUSIONS: Our models not only detect the calcifications automatically, but also have value in predicting cervical LNM risk of PTC patients, thereby making it possible to investigate the relationship between calcifications and highly invasive PTC in detail. CLINICAL RELEVANCE STATEMENT: Due to the high association of US microcalcifications with thyroid cancers, our model will contribute to the differential diagnosis of thyroid nodules in daily practice. KEY POINTS: • We developed an ML-based network model for automatically detecting and quantifying calcifications within thyroid nodules in US images. • Three novel parameters for quantifying US calcifications were defined and verified. • These US calcification parameters showed value in predicting the risk of cervical LNM in PTC patients.

Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT.

BACKGROUND: Adaptive radiation treatment (ART) for locally advanced pancreatic cancer (LAPC) requires consistently accurate segmentation of the extremely mobile gastrointestinal (GI) organs at risk (OAR) including the stomach, duodenum, large and small bowel. Also, due to lack of sufficiently accurate and fast deformable image registration (DIR), accumulated dose to the GI OARs is currently only approximated, further limiting the ability to more precisely adapt treatments. PURPOSE: Develop a 3-D Progressively refined joint Registration-Segmentation (ProRSeg) deep network to deformably align and segment treatment fraction magnetic resonance images (MRI)s, then evaluate segmentation accuracy, registration consistency, and feasibility for OAR dose accumulation. METHOD: ProRSeg was trained using five-fold cross-validation with 110 T2-weighted MRI acquired at five treatment fractions from 10 different patients, taking care that same patient scans were not placed in training and testing folds. Segmentation accuracy was measured using Dice similarity coefficient (DSC) and Hausdorff distance at 95th percentile (HD95). Registration consistency was measured using coefficient of variation (CV) in displacement of OARs. Statistical comparison to other deep learning and iterative registration methods were done using the Kruskal-Wallis test, followed by pair-wise comparisons with Bonferroni correction applied for multiple testing. Ablation tests and accuracy comparisons against multiple methods were done. Finally, applicability of ProRSeg to segment cone-beam CT (CBCT) scans was evaluated on a publicly available dataset of 80 scans using five-fold cross-validation. RESULTS: ProRSeg processed 3D volumes (128 × 192 × 128) in 3 s on a NVIDIA Tesla V100 GPU. It's segmentations were significantly more accurate ( p < 0.001 $p<0.001$ ) than compared methods, achieving a DSC of 0.94 ±0.02 for liver, 0.88±0.04 for large bowel, 0.78±0.03 for small bowel and 0.82±0.04 for stomach-duodenum from MRI. ProRSeg achieved a DSC of 0.72±0.01 for small bowel and 0.76±0.03 for stomach-duodenum from public CBCT dataset. ProRSeg registrations resulted in the lowest CV in displacement (stomach-duodenum C V x $CV_{x}$ : 0.75%, C V y $CV_{y}$ : 0.73%, and C V z $CV_{z}$ : 0.81%; small bowel C V x $CV_{x}$ : 0.80%, C V y $CV_{y}$ : 0.80%, and C V z $CV_{z}$ : 0.68%; large bowel C V x $CV_{x}$ : 0.71%, C V y $CV_{y}$ : 0.81%, and C V z $CV_{z}$ : 0.75%). ProRSeg based dose accumulation accounting for intra-fraction (pre-treatment to post-treatment MRI scan) and inter-fraction motion showed that the organ dose constraints were violated in four patients for stomach-duodenum and for three patients for small bowel. Study limitations include lack of independent testing and ground truth phantom datasets to measure dose accumulation accuracy. CONCLUSIONS: ProRSeg produced more accurate and consistent GI OARs segmentation and DIR of MRI and CBCTs compared to multiple methods. Preliminary results indicates feasibility for OAR dose accumulation using ProRSeg.

Deep learning-based dominant index lesion segmentation for MR-guided radiation therapy of prostate cancer.

BACKGROUND: Dose escalation radiotherapy enables increased control of prostate cancer (PCa) but requires segmentation of dominant index lesions (DIL). This motivates the development of automated methods for fast, accurate, and consistent segmentation of PCa DIL. PURPOSE: To construct and validate a model for deep-learning-based automatic segmentation of PCa DIL defined by Gleason score (GS) ≥3+4 from MR images applied to MR-guided radiation therapy. Validate generalizability of constructed models across scanner and acquisition differences. METHODS: Five deep-learning networks were evaluated on apparent diffusion coefficient (ADC) MRI from 500 lesions in 365 patients arising from internal training Dataset 1 (156 lesions in 125 patients, 1.5Tesla GE MR with endorectal coil), testing using Dataset 1 (35 lesions in 26 patients), external ProstateX Dataset 2 (299 lesions in 204 patients, 3Tesla Siemens MR), and internal inter-rater Dataset 3 (10 lesions in 10 patients, 3Tesla Philips MR). The five networks include: multiple resolution residually connected network (MRRN) and MRRN regularized in training with deep supervision implemented into the last convolutional block (MRRN-DS), Unet, Unet++, ResUnet, and fast panoptic segmentation (FPSnet) as well as fast panoptic segmentation with smoothed labels (FPSnet-SL). Models were evaluated by volumetric DIL segmentation accuracy using Dice similarity coefficient (DSC) and the balanced F1 measure of detection accuracy, as a function of lesion aggressiveness and size (Dataset 1 and 2), and accuracy with respect to two-raters (on Dataset 3). Upon acceptance for publication segmentation models will be made available in an open-source GitHub repository. RESULTS: In general, MRRN-DS more accurately segmented tumors than other methods on the testing datasets. MRRN-DS significantly outperformed ResUnet in Dataset2 (DSC of 0.54 vs. 0.44, p < 0.001) and the Unet++ in Dataset3 (DSC of 0.45 vs. p = 0.04). FPSnet-SL was similarly accurate as MRRN-DS in Dataset2 (p = 0.30), but MRRN-DS significantly outperformed FPSnet and FPSnet-SL in both Dataset1 (0.60 vs. 0.51 [p = 0.01] and 0.54 [p = 0.049] respectively) and Dataset3 (0.45 vs. 0.06 [p = 0.002] and 0.24 [p = 0.004] respectively). Finally, MRRN-DS produced slightly higher agreement with experienced radiologist than two radiologists in Dataset 3 (DSC of 0.45 vs. 0.41). CONCLUSIONS: MRRN-DS was generalizable to different MR testing datasets acquired using different scanners. It produced slightly higher agreement with an experienced radiologist than that between two radiologists. Finally, MRRN-DS more accurately segmented aggressive lesions, which are generally candidates for radiative dose ablation.

Evaluating Germline Testing Panels in Southern African Males With Advanced Prostate Cancer.

BACKGROUND: Germline testing for prostate cancer is on the increase, with clinical implications for risk assessment, treatment, and management. Regardless of family history, NCCN recommends germline testing for patients with metastatic, regional, very-high-risk localized, and high-risk localized prostate cancer. Although African ancestry is a significant risk factor for aggressive prostate cancer, due to a lack of available data no testing criteria have been established for ethnic minorities. PATIENTS AND METHODS: Through deep sequencing, we interrogated the 20 most common germline testing panel genes in 113 Black South African males presenting with largely advanced prostate cancer. Bioinformatic tools were then used to identify the pathogenicity of the variants. RESULTS: After we identified 39 predicted deleterious variants (16 genes), further computational annotation classified 17 variants as potentially oncogenic (12 genes; 17.7% of patients). Rare pathogenic variants included CHEK2 Arg95Ter, BRCA2 Trp31Arg, ATM Arg3047Ter (2 patients), and TP53 Arg282Trp. Notable oncogenic variants of unknown pathogenicity included novel BRCA2 Leu3038Ile in a patient with early-onset disease, whereas patients with FANCA Arg504Cys and RAD51C Arg260Gln reported a family history of prostate cancer. Overall, rare pathogenic and early-onset or familial-associated oncogenic variants were identified in 6.9% (5/72) and 9.2% (8/87) of patients presenting with a Gleason score ≥8 or ≥4 + 3 prostate cancer, respectively. CONCLUSIONS: In this first-of-its-kind study of southern African males, we provide support of African inclusion for advanced, early-onset, and familial prostate cancer genetic testing, indicating clinical value for 30% of current gene panels. Recognizing current panel limitations highlights an urgent need to establish testing guidelines for men of African ancestry. We provide a rationale for considering lowering the pathologic diagnostic inclusion criteria and call for further genome-wide interrogation to ensure the best possible African-relevant prostate cancer gene panel.

Mitochondrial division inhibitor (mdivi-1) inhibits proliferation and epithelial-mesenchymal transition via the NF-κB pathway in thyroid cancer cells.

Excessively fragmented mitochondria have been reported in thyroid cancer (TC). Mitochondrial division inhibitor (mdivi-1), a putative inhibitor of dynamin-related protein 1 (Drp1), prevents mitochondrial fission and thereby restricts cell proliferation across several types of primary cancer. However, the role of mdivi-1 on TC has not been sufficiently studied. This research is intended to explore the therapeutic effect of mdivi-1 in TC cells. Results demonstrated that highly invasive TC cells displayed excessive mitochondrial fission with more fragmented mitochondria. Treatment with mdivi-1 inhibited mitochondrial fission in 8505C cells as indicated by transmission electron microscope (TEM). It also impaired the proliferation and increased apoptosis in 8505C and K1 cells as shown by plate cloning assay, cell viability assay, and apoptosis assay. Mdivi-1 treatment also attenuated migratory and invasive abilities in 8505C and K1 cells as shown by the transwell assay and the wound healing assay. And we noticed the same inhibition of mdivi-1 in cell migration and cell viability after the knockdown of Drp1 in 8505C cells. This demonstrated that mdivi-1 exerted an anti-tumor effect independently of Drp1 in 8505C cells. Moreover, mdivi-1 treatment reversed epithelial-mesenchymal transition (EMT) by inhibiting the NF-κB pathway in 8505C cells. The present findings demonstrate that mdivi-1 has a therapeutic role in thyroid carcinoma.

Seq2Morph: A deep learning deformable image registration algorithm for longitudinal imaging studies and adaptive radiotherapy.

PURPOSE: To simultaneously register all the longitudinal images acquired in a radiotherapy course for analyzing patients' anatomy changes for adaptive radiotherapy (ART). METHODS: To address the unique needs of ART, we designed Seq2Morph, a novel deep learning-based deformable image registration (DIR) network. Seq2Morph was built upon VoxelMorph which is a general-purpose framework for learning-based image registration. The major upgrades are (1) expansion of inputs to all weekly cone-beam computed tomography (CBCTs) acquired for monitoring treatment responses throughout a radiotherapy course, for registration to their planning CT; (2) incorporation of 3D convolutional long short-term memory between the encoder and decoder of VoxelMorph, to parse the temporal patterns of anatomical changes; and (3) addition of bidirectional pathways to calculate and minimize inverse consistency errors (ICEs). Longitudinal image sets from 50 patients, including a planning CT and 6 weekly CBCTs per patient, were utilized for network training and cross-validation. The outputs were deformation vector fields for all the registration pairs. The loss function was composed of a normalized cross-correlation for image intensity similarity, a DICE for contour similarity, an ICE, and a deformation regularization term. For performance evaluation, DICE and Hausdorff distance (HD) for the manual versus predicted contours of tumor and esophagus on weekly basis were quantified and further compared with other state-of-the-art algorithms, including conventional VoxelMorph and large deformation diffeomorphic metric mapping (LDDMM). RESULTS: Visualization of the hidden states of Seq2Morph revealed distinct spatiotemporal anatomy change patterns. Quantitatively, Seq2Morph performed similarly to LDDMM, but significantly outperformed VoxelMorph as measured by GTV DICE: (0.799±0.078, 0.798±0.081, and 0.773±0.078), and 50% HD (mm): (0.80±0.57, 0.88±0.66, and 0.95±0.60). The per-patient inference of Seq2Morph took 22 s, much less than LDDMM (∼30 min). CONCLUSIONS: Seq2Morph can provide accurate and fast DIR for longitudinal image studies by exploiting spatial-temporal patterns. It closely matches the clinical workflow and has the potential to serve both online and offline ART.

Self-supervised 3D anatomy segmentation using self-distilled masked image transformer (SMIT).

Vision transformers efficiently model long-range context and thus have demonstrated impressive accuracy gains in several image analysis tasks including segmentation. However, such methods need large labeled datasets for training, which is hard to obtain for medical image analysis. Self-supervised learning (SSL) has demonstrated success in medical image segmentation using convolutional networks. In this work, we developed a self-distillation learning with masked image modeling method to perform SSL for vision transformers (SMIT) applied to 3D multi-organ segmentation from CT and MRI. Our contribution combines a dense pixel-wise regression pretext task performed within masked patches called masked image prediction with masked patch token distillation to pre-train vision transformers. Our approach is more accurate and requires fewer fine tuning datasets than other pretext tasks. Unlike prior methods, which typically used image sets arising from disease sites and imaging modalities corresponding to the target tasks, we used 3,643 CT scans (602,708 images) arising from head and neck, lung, and kidney cancers as well as COVID-19 for pre-training and applied it to abdominal organs segmentation from MRI pancreatic cancer patients as well as publicly available 13 different abdominal organs segmentation from CT. Our method showed clear accuracy improvement (average DSC of 0.875 from MRI and 0.878 from CT) with reduced requirement for fine-tuning datasets over commonly used pretext tasks. Extensive comparisons against multiple current SSL methods were done. Our code is available at: https://github.com/harveerar/SMIT.git.

Circ_0027446 induces CLDN1 expression to promote papillary thyroid cancer cell malignancy by binding to miR-129-5p.

BACKGROUND: Previous data have shown that circular RNA (circRNA) is a key regulator in papillary thyroid cancer (PTC). However, the role and the detailed mechanism of circ_0027446 in PTC progression have not been reported. METHODS: Circ_0027446, miR-129-5p, claudin 1 (CLDN1), C-myc and MMP2 expression were analyzed by quantitative real-time polymerase chain reaction (qRT-PCR), Western Blot or immunohistochemistry (IHC) assay. Cell viability was evaluated by cell counting kit-8 (CCK-8) assay. Cell proliferation was investigated by 5-Ethynyl-2'-deoxyuridine (EdU) assay and cell colony formation assay. Cell apoptosis, invasion and migration were assessed by flow cytometry analysis, transwell assay and wound-healing assay, respectively. Dual-luciferase reporter assay was conducted to identify the associations among circ_0027446, miR-129-5p and CLDN1. The effect of circ_0027446 on PTC cell malignancy in vivo was revealed by a xenograft mouse model assay. RESULTS: Circ_0027446 and CLDN1 expression were significantly upregulated, while miR-129-5p expression was downregulated in PTC tissues and cells. High circ_0027446 expression was closely associated with the poor prognosis of PTC patients. Circ_0027446 depletion inhibited PTC cell proliferation, migration and invasion but increased cell apoptosis. In addition, circ_0027446 acted as a miR-129-5p sponge, and miR-129-5p bound to CLDN1. Moreover, miR-129-5p inhibitors attenuated circ_0027446 depletion-induced effects in PTC cells. CLDN1 also participated in the regulation of miR-129-5p in PTC cell tumor properties. Importantly, circ_0027446 mediated CLDN1 expression by interacting with miR-129-5p. In vivo data showed that the decreased expression of circ_0027446 led to delayed tumor formation. CONCLUSION: Circ_0027446 contributed to PTC cell tumor properties by regulating the miR-129-5p/CLDN1 pathway, showing circ_0027446 might be a therapeutic target in PTC.

African-specific molecular taxonomy of prostate cancer.

Prostate cancer is characterized by considerable geo-ethnic disparity. African ancestry is a significant risk factor, with mortality rates across sub-Saharan Africa of 2.7-fold higher than global averages1. The contributing genetic and non-genetic factors, and associated mutational processes, are unknown2,3. Here, through whole-genome sequencing of treatment-naive prostate cancer samples from 183 ancestrally (African versus European) and globally distinct patients, we generate a large cancer genomics resource for sub-Saharan Africa, identifying around 2 million somatic variants. Significant African-ancestry-specific findings include an elevated tumour mutational burden, increased percentage of genome alteration, a greater number of predicted damaging mutations and a higher total of mutational signatures, and the driver genes NCOA2, STK19, DDX11L1, PCAT1 and SETBP1. Examining all somatic mutational types, we describe a molecular taxonomy for prostate cancer differentiated by ancestry and defined as global mutational subtypes (GMS). By further including Chinese Asian data, we confirm that GMS-B (copy-number gain) and GMS-D (mutationally noisy) are specific to African populations, GMS-A (mutationally quiet) is universal (all ethnicities) and the African-European-restricted subtype GMS-C (copy-number losses) predicts poor clinical outcomes. In addition to the clinical benefit of including individuals of African ancestry, our GMS subtypes reveal different evolutionary trajectories and mutational processes suggesting that both common genetic and environmental factors contribute to the disparity between ethnicities. Analogous to gene-environment interaction-defined here as a different effect of an environmental surrounding in people with different ancestries or vice versa-we anticipate that GMS subtypes act as a proxy for intrinsic and extrinsic mutational processes in cancers, promoting global inclusion in landmark studies.