To explore the feasibility, short-term efficacies and complications of computed tomography (CT)-guided ¹²⁵I interstitial implant therapy for recurrent ovarian cancer.From October 2009 to November 2010, a total of 25 lesions for 12 patients were diagnosed as recurrent ovarian cancer by positron emission tomography/computed tomography (PET/CT). Among 25 lesions, 21 underwent ¹²⁵I seed implantation. And 4 lesions of liver and spleen in one patient were treated with microwave ablation. Nine patients underwent 2 - 6 cycles of chemotherapy after seeding. There were 11 lesions with diameter > 2 cm and 10 ≤ 2 cm. According to the area of physiologic 18FDG uptake in lesions, the treatment plans were formulated by computerized treatment planning system (TPS) and Memorial Sloan-Ketterin nomograph. The matched peripheral dose (MPD) was 145 Gy in target mass. A median of 20.5 seeds per patient (range: 9 - 45) were implanted. The efficacies were evaluated by CT and 18F-FDG PET/CT findings.One patient died of renal failure while the other patients survived during a median follow-up of 15 mouths (range: 9 - 19). Ten lesions showed complete remission, 6 partial remission and 5 progressive disease. The effective rate was 76.2% (16/21). Compared with those > 2 cm, the lesions ≤ 2 cm in diameter had a better local control rate by Fisher's exact test (P = 0.035). The hepatic and renal lesions treated by microwave ablation showed inactivation on PET/CT. Only one patient suffered from sciatic nerve injury caused by punctuation and numbness and pain of right lower extremity were persistent. There was no onset of the complications of radiation injury, such as intestinal fistula and proctitis.The CT-guided ¹²⁵I interstitial implant therapy for recurrent ovarian cancer yields excellent short-term efficacies with fewer complications. The combined modality of ¹²⁵I interstitial implant and chemotherapy may further improve the patient outcomes.
3563 Background: The early stage breast cancer patients can vary in disease-free survival (DFS), innovative predictors evaluate the prognostic capacity are urgently needed. We aimed to develop and independently validate a radiomics signature based on MRI associated with phenotypes and DFS in patients with breast cancer and to establish a radiomics nomogram that incorporates the radiomics signature and clinicopathological findings using computational algorithms. Methods: In this multicenter, retrospective, cohort study, we analyzed preoperative contrast–enhanced MRI data from the prospective cohort study (n = 123) of patients who had been treated with neoadjuvant chemotherapy in phase 3 trials and independent cohort (n = 438) at the Sun Yat-sen Memorial Hospital as training cohort to develop the radiomic signature, and validated it in validation cohort (Foshan cohort, n = 121; Dongguan cohort, n = 89) between November 17, 2011, and September 21, 2019, and validated in TGCA cohort (n = 84). Machine-learning algorithm to identify robust imaging subtypes and evaluated their clinical and biologic relevance. A nomogram combining the radiomic signature and clinicopathological findings to predict individual survival based on Cox regression model. The primary endpoint was disease-free survival (DFS). This study is registered with ClinicalTrials.gov, number NCT04003558, and Chinese Clinical Trail Registry, number ChiCTR1900024020. Results: A total of 855 breast cancer patients were included. Radiomics signature was generated to classify patients into high-risk and low-risk groups in the Guangzhou training cohort. Patients with low-risk scores in the training cohort had higher DFS (hazard ratio [HR] 0.55, 95% CI 0.31 to 0.99; P= 0.045) than patients with high-risk scores, and validated in in validation cohort (HR 0.14, 95% CI 0.03 to 0.62; P= 0.003). The nomogram combined radiomics score with clinicopathological factors could accurately predict DFS benefits in training cohort (C-index = 0.83; AUC, 1, 2, 3-year were 0.80, 0.85, 0.82, respectively) and validated in validation cohorts. Conclusions: The radiomics signature are significantly associated with the DFS in patients with breast cancer. Combining the radiomics nomogram improved individualized DFS pretiction. Clinical trial information: NCT04003558 .
Objective: To investigate the ultrasonographic features of breast cancer, and analyse its correlation with ER, PR, HER-2 and nm23.
Methods: Female patients with breast cancer from January 2015 to December 2016 were selected and retrospectively analysed ultrasound imaging performance in accordance with the inclusion criteria. And immunohistochemical method was used to detect the expression of ER, PR, HER-2 and nm23 in pathological specimens to analyse its correlation with ultrasonic imaging features.
Results: The positive rate of ER, PR in patients with spiculation in the lumps’ edge was significantly higher than that of patients with no spiculation in the lumps’ edge (P 1 in the lumps was greater than that of patients with aspect ratio ≤ 1 (P<0.05). Whether the lesion of breast cancer had rear echo increased or not had no significant relationship with the positive rate of ER, PR, HER-2 and nm23.
Conclusion: There is correlation between ultrasonic performance in breast cancer and the expression of ER, PR, HER-2 and nm23, and through the features of ultrasound, the expression of ER, PR, HER-2 and nm23 in lesion of tumor can be partly estimated.
For bronchial sleeve and carinal resection and reconstruction during uniportal video-assisted thoracic surgery (VATS), ventilation technique remains a demanding challenge for the anesthesiologists. The ventilation techniques require maintaining adequate gas exchange while providing a good surgical exposure. The case we present was a 58-year-old female with carcinoma in the right upper lobe involving the right main bronchus and the lower trachea. Right upper sleeve lobectomy, carinal resection and reconstruction was performed under uniportal VATS. A modified double-lumen tube (DLT) was inserted to achieve one-lung ventilation, and high-frequency jet ventilation (HFJV) was passed through the DLT to provide oxygenation during the anastomosis without interfering with the surgical procedure. The whole procedure was uneventful. We suggest that the double lumen tube could be modified being a simple and safe option for one-lung ventilation in carinal resection and reconstruction under uniportal VATS.
Abstract Kaposi's sarcoma‐associated herpesvirus (KSHV) causes life‐long latent infection and malignancies, including KS commonly found in AIDS patients. Lytic replication can be induced to kill tumor cells harboring latent KSHV, through viral cytopathic effects and the subsequent antiviral immune responses. Viral FLICE‐inhibitory protein (vFLIP), encoded by KSHV ORF K13, inhibits KSHV lytic reactivation, implying that the competing endogenous RNA (ceRNA) networks regulated by vFLIP can be modulated to induce the lytic reactivation of latent KSHV, a promising strategy for KSHV‐associated malignancies. Here, we performed whole‐transcriptome sequencing to reveal the global landscape of noncoding RNAs and messenger RNAs (mRNAs) in iSLK‐RGB‐BAC16 cells and iSLK‐RGB‐K13 mutant cells. It showed that vFLIP regulated 227 differentially expressed (DE) long non‐coding RNAs (lncRNAs), 57 DE circular RNAs (circRNAs), 20 DE microRNAs (miRNAs), and 1371 DE mRNAs. Enrichment analysis verified that riboflavin metabolism was simultaneously enriched in DE genes related to miRNAs, lncRNAs, and circRNAs. The upregulated hsa‐miR‐378i and hsa‐miR‐3654, and downregulated miR‐4467, miR‐3163, miR‐4451, and miR‐4257 were significantly enriched in the ceRNA complex network, which contained 9 upregulated and 7 downregulated circRNAs, 5 upregulated and 85 downregulated lncRNAs, 5 upregulated and 35 downregulated mRNAs. Finally, we constructed and validated two vFLIP‐regulated ceRNA networks: circRNA hsa_circ_0070049/hsa‐miR‐378i/SPEG/FOXQ1 and lncRNA AL031123.1/hsa‐miR‐378i/SPEG/FOXQ1. Taken together, the two ceRNA networks may mediate KSHV reactivation. These novel findings refreshed the present understanding of ceRNA network in KSHV lytic induction and provided potential therapeutic targets for KSHV‐associated malignancies.
Axillary lymph node metastasis (ALNM) status, typically estimated using an invasive procedure with a high false-negative rate, strongly affects the prognosis of recurrence in breast cancer. However, preoperative noninvasive tools to accurately predict ALNM status and disease-free survival (DFS) are lacking.To develop and validate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic signatures for preoperative identification of ALNM and to assess individual DFS in patients with early-stage breast cancer.This retrospective prognostic study included patients with histologically confirmed early-stage breast cancer diagnosed at 4 hospitals in China from July 3, 2007, to September 21, 2019, randomly divided (7:3) into development and vaidation cohorts. All patients underwent preoperative MRI scans, were treated with surgery and sentinel lymph node biopsy or ALN dissection, and were pathologically examined to determine the ALNM status. Data analysis was conducted from February 15, 2019, to March 20, 2020.Clinical and DCE-MRI radiomic signatures.The primary end points were ALNM and DFS.This study included 1214 women (median [IQR] age, 47 [42-55] years), split into development (849 [69.9%]) and validation (365 [30.1%]) cohorts. The radiomic signature identified ALNM in the development and validation cohorts with areas under the curve (AUCs) of 0.88 and 0.85, respectively, and the clinical-radiomic nomogram accurately predicted ALNM in the development and validation cohorts (AUC, 0.92 and 0.90, respectively) based on a least absolute shrinkage and selection operator (LASSO)-logistic regression model. The radiomic signature predicted 3-year DFS in the development and validation cohorts (AUC, 0.81 and 0.73, respectively), and the clinical-radiomic nomogram could discriminate high-risk from low-risk patients in the development cohort (hazard ratio [HR], 0.04; 95% CI, 0.01-0.11; P < .001) and the validation cohort (HR, 0.04; 95% CI, 0.004-0.32; P < .001) based on a random forest-Cox regression model. The clinical-radiomic nomogram was associated with 3-year DFS in the development and validation cohorts (AUC, 0.89 and 0.90, respectively). The decision curve analysis demonstrated that the clinical-radiomic nomogram displayed better clinical predictive usefulness than the clinical or radiomic signature alone.This study described the application of MRI-based machine learning in patients with breast cancer, presenting novel individualized clinical decision nomograms that could be used to predict ALNM status and DFS. The clinical-radiomic nomograms were useful in clinical decision-making associated with personalized selection of surgical interventions and therapeutic regimens for patients with early-stage breast cancer.
Hepatocellular carcinoma (HCC) is the third-leading cause of cancer deaths globally. Although considerable progress has been made in the treatment, clinical outcomes of HCC patients are still poor. Therefore, it is necessary to find novel prognostic factors upon which prevention and treatment strategies can be formulated. Ficolin-3 (FCN3) protein is a member of the human ficolin family. It activates complement through pathways associated with mannose-binding lectin-associated serine proteases. Herein, we identified that FCN3 was downregulated in HCC tissues and decreased FCN3 expression was closely related to poor prognosis. Overexpression of FCN3 induced apoptosis and inhibited cell proliferation via the p53 signaling pathway. Mechanistically, FCN3 modulated the nuclear translocation of eukaryotic initiation factor 6 (EIF6) by binding ribosome maturation factor (SBDS), which induced ribosomal stress and activation of the p53 pathway. In addition, Y-Box Binding Protein 1 (YBX1) involved in the transcription and translation level regulation of FCN3 to SBDS. Besides, a negative feedback loop in the downstream of FCN3 involving p53, YBX1 and SBDS was identified.
in current clinical practice, the standard evaluation for axillary lymph node (ALN) status in breast cancer has a low efficiency and is based on an invasive procedure that causes operative-associated complications in many patients. Therefore, we aimed to use machine learning techniques to develop an efficient preoperative magnetic resonance imaging (MRI) radiomics evaluation approach of ALN status and explore the association between radiomics and the tumor microenvironment in patients with early-stage invasive breast cancer.in this retrospective multicenter study, three independent cohorts of patients with breast cancer (n = 1,088) were used to develop and validate signatures predictive of ALN status. After applying the machine learning random forest algorithm to select the key preoperative MRI radiomic features, we used ALN and tumor radiomic features to develop the ALN-tumor radiomic signature for ALN status prediction by the support vector machine algorithm in 803 patients with breast cancer from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center (training cohort). By combining ALN and tumor radiomic features with corresponding clinicopathologic information, the multiomic signature was constructed in the training cohort. Next, the external validation cohort (n = 179) of patients from Shunde Hospital of Southern Medical University and Tungwah Hospital of Sun Yat-Sen University, and the prospective-retrospective validation cohort (n = 106) of patients treated with neoadjuvant chemotherapy in prospective phase 3 trials [NCT01503905], were included to evaluate the predictive value of the two signatures, and their predictive performance was assessed by the area under operating characteristic curve (AUC). This study was registered with ClinicalTrials.gov, number NCT04003558.the ALN-tumor radiomic signature for ALN status prediction comprising ALN and tumor radiomic features showed a high prediction quality with AUC of 0·88 in the training cohort, 0·87 in the external validation cohort, and 0·87 in the prospective-retrospective validation cohort. The multiomic signature incorporating tumor and lymph node MRI radiomics, clinical and pathologic characteristics, and molecular subtypes achieved better performance for ALN status prediction with AUCs of 0·90, 0·91, and 0·93 in the training cohort, the external validation cohort, and the prospective-retrospective validation cohort, respectively. Among patients who underwent neoadjuvant chemotherapy in the prospective-retrospective validation cohort, there were significant differences in the key radiomic features before and after neoadjuvant chemotherapy, especially in the gray-level dependence matrix features. Furthermore, there was an association between MRI radiomics and tumor microenvironment features including immune cells, long non-coding RNAs, and types of methylated sites. Interpretation this study presented a multiomic signature that could be preoperatively and conveniently used for identifying patients with ALN metastasis in early-stage invasive breast cancer. The multiomic signature exhibited powerful predictive ability and showed the prospect of extended application to tailor surgical management. Besides, significant changes in key radiomic features after neoadjuvant chemotherapy may be explained by changes in the tumor microenvironment, and the association between MRI radiomic features and tumor microenvironment features may reveal the potential biological underpinning of MRI radiomics.No funding.
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