A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study.

Background: The novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models' generalization ability. Methods: We retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance. Results: The AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05). Conclusion: The nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively.

Discrimination of ground-glass nodular lung adenocarcinoma pathological subtypes via transfer learning: A multicenter study.

BACKGROUND: The surgical approach and prognosis for invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA) of the lung differ. However, they both manifest as identical ground-glass nodules (GGNs) in computed tomography images, and no effective method exists to discriminate them. METHODS: We developed and validated a three-dimensional (3D) deep transfer learning model to discriminate IAC from MIA based on CT images of GGNs. This model uses a 3D medical image pre-training model (MedicalNet) and a fusion model to build a classification network. Transfer learning was utilized for end-to-end predictive modeling of the cohort data of the first center, and the cohort data of the other two centers were used as independent external validation data. This study included 999 lung GGN images of 921 patients pathologically diagnosed with IAC or MIA at three cohort centers. RESULTS: The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). The model had high diagnostic efficacy for the training and validation groups (accuracy: 89%, sensitivity: 95%, specificity: 84%, and AUC: 95% in the training group; accuracy: 88%, sensitivity: 84%, specificity: 93%, and AUC: 92% in the internal validation group; accuracy: 83%, sensitivity: 83%, specificity: 83%, and AUC: 89% in one external validation group; accuracy: 78%, sensitivity: 80%, specificity: 77%, and AUC: 82% in the other external validation group). CONCLUSIONS: Our 3D deep transfer learning model provides a noninvasive, low-cost, rapid, and reproducible method for preoperative prediction of IAC and MIA in lung cancer patients with GGNs. It can help clinicians to choose the optimal surgical strategy and improve the prognosis of patients.

Preoperative prediction of tumor deposits in rectal cancer with clinical-magnetic resonance deep learning-based radiomic models.

Background: This study aimed to establish an effective model for preoperative prediction of tumor deposits (TDs) in patients with rectal cancer (RC). Methods: In 500 patients, radiomic features were extracted from magnetic resonance imaging (MRI) using modalities such as high-resolution T2-weighted (HRT2) imaging and diffusion-weighted imaging (DWI). Machine learning (ML)-based and deep learning (DL)-based radiomic models were developed and integrated with clinical characteristics for TD prediction. The performance of the models was assessed using the area under the curve (AUC) over five-fold cross-validation. Results: A total of 564 radiomic features that quantified the intensity, shape, orientation, and texture of the tumor were extracted for each patient. The HRT2-ML, DWI-ML, Merged-ML, HRT2-DL, DWI-DL, and Merged-DL models demonstrated AUCs of 0.62 ± 0.02, 0.64 ± 0.08, 0.69 ± 0.04, 0.57 ± 0.06, 0.68 ± 0.03, and 0.59 ± 0.04, respectively. The clinical-ML, clinical-HRT2-ML, clinical-DWI-ML, clinical-Merged-ML, clinical-DL, clinical-HRT2-DL, clinical-DWI-DL, and clinical-Merged-DL models demonstrated AUCs of 0.81 ± 0.06, 0.79 ± 0.02, 0.81 ± 0.02, 0.83 ± 0.01, 0.81 ± 0.04, 0.83 ± 0.04, 0.90 ± 0.04, and 0.83 ± 0.05, respectively. The clinical-DWI-DL model achieved the best predictive performance (accuracy 0.84 ± 0.05, sensitivity 0.94 ± 0. 13, specificity 0.79 ± 0.04). Conclusions: A comprehensive model combining MRI radiomic features and clinical characteristics achieved promising performance in TD prediction for RC patients. This approach has the potential to assist clinicians in preoperative stage evaluation and personalized treatment of RC patients.

Predictive Efficacy of a Radiomics Random Forest Model for Identifying Pathological Subtypes of Lung Adenocarcinoma Presenting as Ground-Glass Nodules.

Purpose: To establish and verify the ability of a radiomics prediction model to distinguish invasive adenocarcinoma (IAC) and minimal invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs). Methods: We retrospectively analyzed 118 lung GGN images and clinical data from 106 patients in our hospital from March 2016 to April 2019. All pathological classifications of lung GGN were confirmed as IAC or MIA by two pathologists. R language software (version 3.5.1) was used for the statistical analysis of the general clinical data. ITK-SNAP (version 3.6) and A.K. software (Analysis Kit, American GE Company) were used to manually outline the regions of interest of lung GGNs and collect three-dimensional radiomics features. Patients were randomly divided into training and verification groups (ratio, 7:3). Random forest combined with hyperparameter tuning was used for feature selection and prediction modeling. The receiver operating characteristic curve and the area under the curve (AUC) were used to evaluate model prediction efficacy. The calibration curve was used to evaluate the calibration effect. Results: There was no significant difference between IAC and MIA in terms of age, gender, smoking history, tumor history, and lung GGN location in both the training and verification groups (P>0.05). For each lung GGN, the collected data included 396 three-dimensional radiomics features in six categories. Based on the training cohort, nine optimal radiomics features in three categories were finally screened out, and a prediction model was established. We found that the training group had a high diagnostic efficacy [accuracy, sensitivity, specificity, and AUC of the training group were 0.89 (95%CI, 0.73 - 0.99), 0.98 (95%CI, 0.78 - 1.00), 0.81 (95%CI, 0.59 - 1.00), and 0.97 (95%CI, 0.92-1.00), respectively; those of the validation group were 0.80 (95%CI, 0.58 - 0.93), 0.82 (95%CI, 0.55 - 1.00), 0.78 (95%CI, 0.57 - 1.00), and 0.92 (95%CI, 0.83 - 1.00), respectively]. The model calibration curve showed good consistency between the predicted and actual probabilities. Conclusions: The radiomics prediction model established by combining random forest with hyperparameter tuning effectively distinguished IAC from MIA presenting as GGNs and represents a noninvasive, low-cost, rapid, and reproducible preoperative prediction method for clinical application.

Preoperative Monocyte-to-lymphocyte Ratio Predicts for Intravesical Recurrence in Patients With Urothelial Carcinoma of the Upper Urinary Tract After Radical Nephroureterectomy Without a History of Bladder Cancer.

BACKGROUND: This study aimed to investigate the preoperative monocyte-to-lymphocyte ratio (MLR) as a biomarker for intravesical recurrence (IVR) in upper urinary tract urothelial carcinoma (UTUC) after radical nephroureterectomy (RNU) for the first time. PATIENTS AND METHODS: This study involved the clinical data of 255 patients with UTUC without a history of bladder cancer who had undergone RNU from March 2004 to February 2019 at an academic institution. The associations between MLR and IVR were assessed with Kaplan-Meier method and Cox regression analysis. RESULTS: The median follow-up was 43.93 months. Of the 255 patients, 37 developed IVR during the follow-up period. Kaplan-Meier analysis revealed that patients with high MLR (> 0.22) had poor IVR-free survival (P = .001); this prognostic value was in accordance with patients with high grade and more advanced stage UTUC. Cox regression preoperative models showed that ureteral tumor site (hazard ratio [HR], 2.784; P = .005), surgical approach (HR, 2.745; P = .008), and high MLR (HR, 4.085; P < .001) were an independent risk factor for IVR. These factors were used as a signature to establish a prognostic risk model, which revealed significant differences among the 3 subgroups of patients with low, intermediate, and high risk (P < .001). CONCLUSION: Ureteral tumor site, surgical approach, and preoperative MLR are significant predictors for IVR in patients with UTUC after RNU. MLR may become a useful biomarker to predict IVR in patients with UTUC after RNU.

Enucleation of the Prostate for the Treatment of Benign Prostatic Hyperplasia Using a 980 nm Diode Laser.

In the aging male population, the occurrence of lower urinary tract symptoms (LUTS) caused by benign prostatic hyperplasia (BPH) is a common problem. Here, we introduce a new technique called 980 nm diode laser enucleation (DiLEP) to treat BPH1. Diode lasers can absorb both water and hemoglobin at the same time, so they are good for cutting and hemostasis2. The diode laser was approved by the FDA in 2007, and has been used in the treatment of BPH because of its effective cutting and hemostasis effect3. DiLEP presents several advantages over other techniques, such as TURP, HoLEP, and PVP. During the procedure, we define the boundary of a high-volume prostate and separate it into three lobes with a diode laser by burning two rings and one groove (like a Cupid's arrow). Compared to other procedures, mDiLEP has fewer intraoperative complications, a shorter learning curve, and achieves more tissue resection.

Diagnostic Value of Peripheral Blood miR-374b-5p in Patients with Prostate Cancer.

BACKGROUND: Increased evidence suggested the important role of microRNAs (miRNAs) in the tumorigenesis of prostate cancer (PCa). The aberrant expression of miRNA (miR)-374b-5p has been observed in various types of cancers. The purpose of the current study was to evaluate the relationship between miR-374b-5p expression levels and PCa and to assess the feasibility of using peripheral blood miR-374b-5p as a potential non-invasive biomarker for PCa. METHODS: Total RNA was isolated from the whole-blood samples of 42 PCa patients whole-blood samples, 42 benign prostatic hyperplasia (BPH) patients, and 42 healthy controls (HC). The expression of miR-374b-5p was assessed by reverse transcription quantitative polymerase chain reaction. Normalized data were subjected to the receiver operating characteristic (ROC) and Kaplan-Meier analysis. RESULTS: The expression of peripheral blood miR-374b-5p was significantly higher in PCa patients than in HC individuals and patients with BPH (p < 0.001). Upregulation of miR-374b-5p was observed to be related to certain parameters, including Gleason score > 7 (p < 0.001), and PSA > 20 ng/mL (p < 0.01). To further evaluate the role of miR-374b-5p in patients with PCa, ROC analysis was carried out. Our data showed that peripheral blood miR-374b-5p could screen PCa patients from HC individuals (area under the curve (AUC), 0.851; 95% CI, 0.766 - 0.936; p < 0.001) and patients with BPH (AUC, 0.831; 95% CI, 0.742 - 0.920; p < 0.001). CONCLUSIONS: Increased miR-374b-5p expression in peripheral blood may serve as a potential biomarker to distinguish PCa patients from healthy controls and BPH patients.

Technical Modification of the Terminal Ureter During Total Transperitoneal Laparoscopic Nephroureterectomy for Upper Urinary Tract Urothelial Carcinoma.

Upper tract urothelial carcinoma (UTUC) accounts for 5%-10% of all urothelial tumors. Radical nephroureterectomy is the standard treatment procedure. At present, different choices still exist for treating the ureteral end during laparoscopic ureteral bladder sleeve resection. Our center has adopted a new method for treating the ureteral end. This new method can increase the operating space and reduce the difficulty of the surgery compared with current methods.