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Purpose: The purpose of this pilot prospective study is to examine the gallium-68-prostate-specific membrane antigen-11 ([68Ga]Ga-PSMA-11) positron emission tomography/computed tomography (PET/CT) imaging response in patients with advanced or metastatic hormone-naïve prostate cancer (PC) after 3 months of androgen deprivation therapy (ADT). Methods: We prospectively included men with untreated, clinical stage III or IV PC scheduled to receive ADT for at least 6 months. [68Ga]Ga-PSMA-11 PET/CT images were obtained before the start of ADT and 10–14 weeks thereafter. The following indices were examined: maximum standardized uptake value (SUVmax), mean SUV, PSMA total volume, and PSMA total lesion values of the prostate, nodes, bones, and whole-body. The therapeutic response was assessed using the modified PET response criteria in solid tumors 1.0. A subgroup analysis of patients with the International Society of Urological Pathology (ISUP) grade group 5 versus p < 0.001) after 3 months of ADT. Twenty-four (80%) patients showed partial response. Complete response, stable disease, and disease progression were observed in two patients each. Sixteen patients with ISUP grade group 5 showed a less prominent SUVmax reduction (p = 0.006), and none of them reached complete response. Conclusions: Three months of ADT in patients with untreated, advanced PC significantly reduced PSMA PET/CT indices. While most participants partially responded to ADT, patients with ISUP grade group 5 showed a less prominent SUVmax reduction. Collectively, our pilot results indicate that [68Ga]Ga-PSMA-11 PET/CT imaging holds promise to monitor treatment response after the first three months of ADT.

Background: The existing staging systems of uterine leiomyosarcoma (uLMS) cannot classify the patients into four non-overlapping prognostic groups. This study aimed to develop a prediction model to predict the three-year survival status of uLMS. Methods: In total, 201 patients with uLMS who had been treated between June 1993 and January 2014, were analyzed. Potential prognostic indicators were identified by univariate models followed by multivariate analyses. Prediction models were constructed by binomial regression with 3-year survival status as a binary outcome, and the final model was validated by internal cross-validation. Results: Nine potential parameters, including age, log tumor diameter, log mitotic count, cervical involvement, parametrial involvement, lymph node metastasis, distant metastasis, tumor circumscription and lymphovascular space invasion were identified. 110 patients had complete data to build the prediction models. Age, log tumor diameter, log mitotic count, distant metastasis, and circumscription were significantly correlated with the 3-year survival status. The final model with the lowest Akaike’s Information Criterion (117.56) was chosen and the cross validation estimated prediction accuracy was 0.745. Conclusion: We developed a prediction model for uLMS based on five readily available clinicopathologic parameters. This might provide a personalized prediction of the 3-year survival status and guide the use of adjuvant therapy, a cancer surveillance program, and future studies.

Mutational profiling of patients’ tumors has suggested that the development of oral cavity squamous cell carcinoma (OCSCC) is driven by multiple genes in multiple pathways. This study aimed to examine the association between genomic alterations and clinical outcomes in patients with advanced stages OCSCC to facilitate prognostic stratification. We re-analyzed our previous whole-exome sequencing data from 165 long-term follow-ups of stages III and IV patients with OCSCC. Their frequent mutations were mapped to 10 oncogenic signaling pathways. Clinicopathological risk factors, relapse, and survival were analyzed to identify the genetic factors associated with advanced OCSCC. Frequent genetic alterations included point mutations in TP53, FAT1, NOTCH1, CASP8, CDKN2A, HRAS, PIK3CA, KMT2B (also known as MLL4), and LINC00273; amplified segments in CCND1, EGFR, CTTN, and FGFR1; and lost segments in CDKN2A, ADAM3A, and CFHR1/CFHR4. Comprehensive analysis of genetic alterations revealed that subgroups based on mutational signatures had a significant negative impact on disease-free survival (p = 0.0005) and overall survival (p = 0.0024). Several important signaling pathways were identified to be frequently genetically altered in our cohort. A specific subgroup of patients with alterations in NOTCH, RTK/RAS/MAPK, and TGF-beta pathways that had a significantly negative impact on disease-free survival (p = 0.0009). Thirty percent of samples had multiple targetable mutations in multiple pathways, indicating opportunities for novel therapy.

Precise risk stratification in lymphadenectomy is important for patients with endometrial cancer (EC), to balance the therapeutic benefit against the operation-related morbidity and mortality. We aimed to investigate added values of computer-aided segmentation and machine learning based on clinical parameters and diffusion-weighted imaging radiomics for predicting lymph node (LN) metastasis in EC. This prospective observational study included 236 women with EC (mean age ± standard deviation, 51.2 ± 11.6 years) who underwent magnetic resonance (MR) imaging before surgery during July 2010–July 2018, randomly split into training (n = 165) and test sets (n = 71). A decision-tree model was constructed based on mean apparent diffusion coefficient (ADC) value of the tumor (cutoff, 1.1 × 10−3 mm2/s), skewness of the relative ADC value (cutoff, 1.2), short-axis diameter of LN (cutoff, 1.7 mm) and skewness ADC value of the LN (cutoff, 7.2 × 10−2), as well as tumor grade (1 vs. 2 and 3), and clinical tumor size (cutoff, 20 mm). The sensitivity and specificity of the model were 94% and 80% for the training set and 86%, 78% for the independent testing set, respectively. The areas under the receiver operating characteristics curve (AUCs) of the decision-tree was 0.85—significantly higher than the mean ADC model (AUC = 0.54) and LN short-axis diameter criteria (AUC = 0.62) (both p < 0.0001). We concluded that a combination of clinical and MR radiomics generates a prediction model for LN metastasis in EC, with diagnostic performance surpassing the conventional ADC and size criteria.

Chromosomal instability (CIN) of gastric cancer is correlated with distinct outcomes. This study aimed to investigate the role of computed tomography (CT) imaging traits in predicting the CIN status of gastric cancer. We screened 443 patients in the Cancer Genome Atlas gastric cancer cohort to filter 40 patients with complete CT imaging and genomic data as the training cohort. CT imaging traits were subjected to logistic regression to select independent predictors for the CIN status. For the validation cohort, we prospectively enrolled 18 gastric cancer patients for CT and tumor genomic analysis. The imaging predictors were tested in the validation cohort using receiver operating characteristic curve (ROC) analysis. Thirty patients (75%) in the training cohort and 9 patients (50%) in the validation cohort had CIN subtype gastric cancers. Smaller tumor diameter (p = 0.017) and acute tumor transition angle (p = 0.045) independently predict CIN status in the training cohort. In the validation cohort, acute tumor transition angle demonstrated the highest accuracy, sensitivity, and specificity of 88.9%, 88.9%, and 88.9%, respectively, and areas under ROC curve of 0.89. In conclusion, this pilot study showed acute tumor transition angle on CT images may predict the CIN status of gastric cancer.