Value of texture analysis based on dynamic contrast-enhanced magnetic resonance imaging in preoperative assessment of extramural venous invasion in rectal cancer.

OBJECTIVE: Accurate preoperative assessment of extramural vascular invasion (EMVI) is critical for the treatment and prognosis of rectal cancer. The aim of our research was to develop an assessment model by texture analysis for preoperative prediction of EMVI. MATERIALS AND METHODS: This study enrolled 44 rectal patients as train cohort, 7 patients as validation cohort and 18 patients as test cohort. A total of 236 texture features from DCE MR imaging quantitative parameters were extracted for each patient (59 features of Ktrans, Kep, Ve and Vp), and key features were selected by least absolute shrinkage and selection operator regression (LASSO). Finally, clinical independent risk factors, conventional MRI assessment, and T-score were incorporated to construct an assessment model using multivariable logistic regression. RESULTS: The T-score calculated using the 4 selected key features were significantly correlated with EMVI (p < 0.010). The area under the receiver operating characteristic curve (AUC) was 0.797 for discriminating between EMVI-positive and EMVI-negative patients with a sensitivity of 88.2% and specificity of 70.4%. The conventional MRI assessment of EMVI had a sensitivity of 23.53% and a specificity of 96.30%. The assessment model showed a greatly improved performance with an AUC of 0.954 (sensitivity, 88.2%; specificity, 92.6%) in train cohort, 0.833 (sensitivity, 66.7%; specificity, 100%) in validation cohort and 0.877 in test cohort, respectively. CONCLUSIONS: The assessment model showed an excellent performance in preoperative assessment of EMVI. It demonstrates strong potential for improving the accuracy of EMVI assessment and provide a reliable basis for individualized treatment decisions.

Computed Tomography Radiomic Nomogram for Preoperative Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma.

Objectives: Determining the presence of extrathyroidal extension (ETE) is important for patients with papillary thyroid carcinoma (PTC) in selecting the proper surgical approaches. This study aimed to explore a radiomic model for preoperative prediction of ETE in patients with PTC. Methods: The study included 624 PTC patients (without ETE, n = 448; with minimal ETE, n = 52; with gross ETE, n = 124) whom were divided randomly into training (n = 437) and validation (n = 187) cohorts; all data were gathered between January 2016 and November 2017. Radiomic features were extracted from computed tomography (CT) images of PTCs. Key radiomic features were identified and incorporated into a radiomic signature. Combining the radiomic signature with clinical risk factors, a radiomic nomogram was constructed using multivariable logistic regression. Delong test was used to compare different receiver operating characteristic curves. Results: Five key radiomic features were incorporated into the radiomic signature, which were significantly associated with ETE (p < 0.001 for both cohorts) and slightly better than clinical model integrating significant clinical risk factors in the training cohort (area under the receiver operating characteristic curve (AUC), 0.791 vs. 0.778; F1 score, 0.729 vs. 0.714) and validation cohort (AUC, 0.772 vs. 0.756; F1 score, 0.710 vs. 0.692). The radiomic nomogram significantly improved predictive value in the training cohort (AUC, 0.837, p < 0.001; F1 score, 0.766) and validation cohort (AUC, 0.812, p = 0.024; F1 score, 0.732). Conclusions: The radiomic nomogram significantly improved the preoperative prediction of ETE in PTC patients. It indicated that radiomics could be a valuable method in PTC research.

Synergistic triple-combination therapy with hyaluronic acid-shelled PPy/CPT nanoparticles results in tumor regression and prevents tumor recurrence and metastasis in 4T1 breast cancer.

Breast cancer is characterized by high aggression, poor prognosis, and high recurrence rate. Early detection and specific targeted treatment with less toxicity are the ultimate goals for breast cancer therapy. To improve antitumor therapeutic effects, we developed a novel polypyrrole nanoparticle using the near infrared dye IRDye800CW with camptothecin (CPT)-conjugated hyaluronic acid (HA) shell (PPy@CPT-HA-IRDye800CW) and performed a photothermal therapy (PTT), along with chemotherapy, guided by fluorescence and photoacoustic dual-modality imaging, in combination with immunotherapy. Irradiation with near infrared (NIR) light offered a strong PTT effect and promoted CPT drug release in tumors. Moreover, we found that chemo-photothermal therapy with PPy@CPT-HA-IRDye800CW NPs, in combination with immune checkpoint inhibitor anti-PD-L1 immunotherapy, synergistically enhanced the anti-tumor immune response, thereby eliminating primary breast cancer and preventing tumor metastases and recurrences in 4T1 tumor-bearing mice. This approach may provide important clues for the clinical management of breast cancer and other malignant tumors.