POSS Engineering of Multifunctional Nanoplatforms for Chemo-Mild Photothermal Synergistic Therapy.

Chemo-mild photothermal synergistic therapy can effectively inhibit tumor growth under mild hyperthermia, minimizing damage to nearby healthy tissues and skin while ensuring therapeutic efficacy. In this paper, we develop a multifunctional study based on polyhedral oligomeric sesquisiloxane (POSS) that exhibits a synergistic therapeutic effect through mild photothermal and chemotherapy treatments (POSS-SQ-DOX). The nanoplatform utilizes SQ-N as a photothermal agent (PTA) for mild photothermal, while doxorubicin (DOX) serves as the chemotherapeutic drug for chemotherapy. By incorporating POSS into the nanoplatform, we successfully prevent the aggregation of SQ-N in aqueous solutions, thus maintaining its excellent photothermal properties both in vitro and in vivo. Furthermore, the introduction of polyethylene glycol (PEG) significantly enhances cell permeability, which contributes to the remarkable therapeutic effect of POSS-SQ-DOX NPs. Our studies on the photothermal properties of POSS-SQ-DOX NPs demonstrate their high photothermal conversion efficiency (62.3%) and stability, confirming their suitability for use in mild photothermal therapy. A combination index value (CI = 0.72) verified the presence of a synergistic effect between these two treatments, indicating that POSS-SQ-DOX NPs exhibited significantly higher cell mortality (74.7%) and tumor inhibition rate (72.7%) compared to single chemotherapy and mild photothermal therapy. This observation highlights the synergistic therapeutic potential of POSS-SQ-DOX NPs. Furthermore, in vitro and in vivo toxicity tests suggest that the absence of cytotoxicity and excellent biocompatibility of POSS-SQ-DOX NPs provide a guarantee for clinical applications. Therefore, utilizing near-infrared light-triggering POSS-SQ-DOX NPs can serve as chemo-mild photothermal PTA, while functionalized POSS-SQ-DOX NPs hold great promise as a novel nanoplatform that may drive significant advancements in the field of chemo-mild photothermal therapy.

Diffusion-weighted MRI for predicting pathologic response to neoadjuvant chemotherapy in breast cancer: evaluation with mono-, bi-, and stretched-exponential models.

BACKGROUND: To investigate the performance of diffusion-weighted (DW) MRI with mono-, bi- and stretched-exponential models in predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) for breast cancer, and further outline a predictive model of pCR combining DW MRI parameters, contrast-enhanced (CE) MRI findings, and/or clinical-pathologic variables. METHODS: In this retrospective study, 144 women who underwent NACT and subsequently received surgery for invasive breast cancer were included. Breast MRI including multi-b-value DW imaging was performed before (pre-treatment), after two cycles (mid-treatment), and after all four cycles (post-treatment) of NACT. Quantitative DW imaging parameters were computed according to the mono-exponential (apparent diffusion coefficient [ADC]), bi-exponential (pseudodiffusion coefficient and perfusion fraction), and stretched-exponential (distributed diffusion coefficient and intravoxel heterogeneity index) models. Tumor size and relative enhancement ratio of the tumor were measured on contrast-enhanced MRI at each time point. Pre-treatment parameters and changes in parameters at mid- and post-treatment relative to baseline were compared between pCR and non-pCR groups. Receiver operating characteristic analysis and multivariate regression analysis were performed. RESULTS: Of the 144 patients, 54 (37.5%) achieved pCR after NACT. Overall, among all DW and CE MRI measures, flow-insensitive ADC change (ΔADC200,1000) at mid-treatment showed the highest diagnostic performance for predicting pCR, with an area under the receiver operating characteristic curve (AUC) of 0.831 (95% confidence interval [CI]: 0.747, 0.915; P < 0.001). The model combining pre-treatment estrogen receptor and human epidermal growth factor receptor 2 statuses and mid-treatment ΔADC200,1000 improved the AUC to 0.905 (95% CI: 0.843, 0.966; P < 0.001). CONCLUSION: Mono-exponential flow-insensitive ADC change at mid-treatment was a predictor of pCR after NACT in breast cancer.

Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient.

PURPOSE: To investigate the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in capturing breast lesion heterogeneity and determine which ADC metric may help best differentiate benign from malignant breast mass lesions at 3.0T magnetic resonance imaging (MRI). MATERIALS AND METHODS: We retrospectively included 101 women with breast mass lesions (benign:malignant = 36:65) who underwent 3.0T diffusion-weighted imaging (DWI) and subsequently had histopathologic confirmation. ADC histogram parameters, including the mean, minimum, maximum, 10th/25th/50th/75th/90th percentile, skewness, kurtosis, and entropy ADCs, were derived for the whole-lesion volume in each patient. Mann-Whitney U-test, univariate and multivariate logistic regression, area under the receiver-operating characteristic curve (Az ), intraclass correlation coefficient (ICC), and Bland-Altman test were used for statistical analysis. RESULTS: Mean, minimum, maximum, and 10th/25th/50th/75th/90th percentile ADCs were significantly lower (all P < 0.0001), while skewness and entropy ADCs were significantly higher (P < 0.001 and P = 0.001, respectively) in malignant lesions compared with benign ones. The Az values of minimum and 25th percentile ADCs were significantly higher than that of mean ADC (P = 0.0194 and P = 0.0154, respectively) or that of median ADC (P = 0.0300 and P = 0.0401, respectively), indicating that minimum and 25th percentile ADCs may be more accurate for lesion discrimination. Multivariate logistic regression showed that the minimum ADC was the unique independent predictor of breast malignancy. Minimum and 25th percentile ADCs had excellent interobserver agreement (ICC = 0.943 and 0.989, respectively; narrow width of 95% limits of agreement). CONCLUSION: These results suggest that whole-lesion ADC histogram analysis may facilitate the differentiation between benign and malignant breast mass lesions.

A shortcut weighted fusion pyramid network for microcalcification detection in breast mammograms.

BACKGROUND: High-precision detection for individual and clustered microcalcifications in mammograms is important for the early diagnosis of breast cancer. Large-scale differences between the two types and low-contrast images are major difficulties faced by radiologists when performing diagnoses. OBJECTIVE: Deep learning-based methods can provide end-to-end solutions for efficient detection. However, multicenter data bias, the low resolution of network inputs, and scale differences between microcalcifications lead to low detection rates. Aiming to overcome the aforementioned limitations, we propose a pyramid feature network for microcalcification detection in mammograms, MicroDMa, with adaptive image adjustment and shortcut connections. METHODS: First, mammograms from multiple centers are represented as histograms and cropped by adaptive image adjustment, which mitigates the impact of dataset bias. Second, the proposed shortcut connection pyramid network ensures that the feature map contains more information for multiscale objects, while a shortcut path that jumps over layers enhances the efficiency of feature propagation from bottom to top. Third, the weights of each feature map at different scales in the fusion are trainable; thus, the network can automatically learn the contributions of all feature maps in the fusion stage. RESULT: Experiments were conducted on our in-house dataset and the public dataset INbreast. When the average number of positives per image is one on the in-house dataset, the recall rates of MicroDMa are the 96.8% for individual microcalcification and 98.9% for clustered microcalcification, which are higher than 69.1% and 91.2% achieved by recent deep learning model. Free-response receiver operating characteristic curve of MicroDMa is also higher than other methods when models are performed on INbreast. CONCLUSION: MicroDMa network is better than other methods and it can effectively help radiologists detect and identify two types of microcalcifications in clinical applications.

Genomic Profiling and Response to Immune Checkpoint Inhibition plus Tyrosine Kinase Inhibition in FH-Deficient Renal Cell Carcinoma.

BACKGROUND: FH-deficient renal cell carcinoma (RCC) is a rare and exceptionally aggressive RCC subtype. There is currently limited understanding of the molecular alterations, pathogenesis, survival outcomes, and systemic therapy efficacy for this cancer. OBJECTIVE: To perform a retrospective multicenter analysis of molecular profiling and clinical outcomes for patients with FH-deficient RCC, with an emphasis on treatment response to first-line immune checkpoint inhibitor plus tyrosine kinase inhibitor (ICI/TKI) versus bevacizumab plus erlotinib (Bev/Erlo) combination therapy in patients with advanced disease. DESIGN, SETTING, AND PARTICIPANTS: The study included 77 cases of FH-deficient RCC from 15 centers across China. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Clinical characteristics, molecular correlates, 18F-fluorodeoxyglucose positron emission tomography/computed tomography imaging, and treatment outcomes were analyzed. RESULTS AND LIMITATIONS: A total of 77 patients were identified, including 70 cases with a germline FH alteration (hereditary leiomyomatosis RCC syndrome [HLRCC]-associated RCC) and seven patients with somatic FH loss. Recurrent pathogenic alterations were found in NF2 (six/57, 11%), CDH1 (six/57, 11%), PIK3CA (six/57, 11%), and TP53 (five/57, 8.8%). Sixty-seven patients were evaluable for response to first-line systemic therapy with Bev/Erlo (n = 12), TKI monotherapy (n = 29), or ICI/TKI (n = 26). ICI/TKI combination therapy was associated with more favorable overall survival on systemic treatment (hazard ratio [HR] 0.19, 95% confidence interval [CI] 0.04-0.90) and progression-free survival on first-line therapy (HR 0.22, 95% CI 0.07-0.71) compared to Bev/Erlo combination therapy. The main limitation is the retrospective study design. CONCLUSIONS: We described the genomic characteristics of FH-deficient RCC in an Asian population and observed a favorable response to ICI/TKI combinational therapy among patients with advanced disease. PATIENT SUMMARY: This real-world study provides evidence supporting the antitumour activity of combining molecular targeted therapy plus immunotherapy for kidney cancer deficient in fumarate hydratase. Further studies are needed to investigate the efficacy of this combination strategy in this rare cancer.

The predictive value of DKI in breast cancer: Does tumour subtype affect pathological response evaluations?

PURPOSE: To explore the differences in quantitative parameters based on diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) between different immunohistochemical indicator statuses and their predictive value for neoadjuvant chemotherapy (NAC) among different phenotypes of breast cancer. METHODS: Eighty-one breast cancer patients who underwent NAC were enrolled in this retrospective study. Correlations between diffusion parameters and immunohistochemical indicators were determined using Spearman's test, and receiver operating characteristic (ROC) curves were constructed to assess the apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) in predicting the pathologic complete response (PCR). RESULTS: Correlations were observed between MK values and hormone receptor (HR) expression (oestrogen receptor (ER): r = 0.315 and progesterone receptor (PR): r = 0.268). The parameters ADC(0,1000), MK, and MD all showed correlations with Ki67 expression (r = 0.276, 0.316 and - 0.224, respectively). ER and Ki67 expression and the parameters MD and MK were significantly different between the PCR and non-PCR groups (AUC = 0.783, 0.688, 0.649 and 0.684, respectively). After splitting patients into subgroups, no significant differences were observed between the PCR and non-PCR groups with human epidermal growth factor receptor 2 (HER2) + and triple-negative (TN) breast cancer. However, we were surprised to find that ADC(0, 1000), MD, and MK were significantly different between different remission groups with HR+/HER2+ subtypes, and the AUCs of each parameter reached 0.794, 0.825, and 0.712, respectively. CONCLUSION: MK was correlated with HR expression. ADC(0, 1000) and DKI were correlated with Ki67 expression. ADC(0, 1000) and the non-Gaussian diffusion model are suitable for predicting PCR in patients with HR+/HER2+ breast cancer before NAC.

Using the apparent diffusion coefficient histogram analysis to predict response to neoadjuvant chemotherapy in patients with breast cancer: comparison among three region of interest selection methods.

Background: The apparent diffusion coefficient (ADC) value using histogram analysis is helpful to predict responses to neoadjuvant chemotherapy (NAC) in breast cancer. However, the measurement method has not reached a consensus. This study was to assess the diagnostic performance of the ADC histogram analysis at predicting patient response prior to NAC in breast cancer patients using different region of interest (ROI) selection methods. Methods: A total of 75 patients who underwent diffusion weighted imaging (DWI) prior to NAC were retrospectively enrolled from February 2017 to December 2019. Images were measured using small 2-dimensional (2D) ROI, large 2D ROI, and volume ROI methods. The measurement time and ROI size were recorded. Histopathologic responses were acquired using the Miller-Payne grading system after surgery. The inter- and intra-observer repeatability was analyzed and the ADC histogram values from the three ROI methods were compared. The efficacy of each method at predicting patient response prior to NAC was assessed using the area under the receiver operating characteristic curve (AUC) for the whole study population and subgroups according to molecular subtype. Results: Among the 75 enrolled patients, 26 (34.67%) were responsive to NAC therapy. The ADC histogram values were significantly different among the three ROI methods (P≤0.038). Inter- and intra-observer repeatability of the large 2D ROI method and the volume ROI method was generally greater than that observed with the 2D ROI method. The 10% ADC value of the large 2D ROI method showed the greatest AUC (0.701) in the whole study population and in the luminal subgroup (AUC 0.804). The volume ROI method required significantly more time than the other two ROI methods (P<0.001). Conclusions: The small 2D ROI method is not appropriate for predicting response prior to NAC in breast cancer patients due to the poor repeatability. When choosing the ROI method and the histogram parameters for predicting response prior to NAC in breast cancer patients using ADC-derived histogram analysis, 10% of the large 2D ROI method is recommended, especially in luminal A subtype patients.

Dual Energy Spectral CT Imaging for Colorectal Cancer Grading: A Preliminary Study.

OBJECTIVES: To assess the diagnostic value of dual energy spectral CT imaging for colorectal cancer grading using the quantitative iodine density measurements in both arterial phase (AP) and venous phase (VP). METHODS: 81 colorectal cancer patients were divided into two groups based on their pathological findings: a low grade group including well (n = 13) and moderately differentiated cancer (n = 24), and a high grade group including poorly differentiated (n = 42) and signet ring cell cancer (n = 2). Iodine density (ID) in the lesions was derived from the iodine-based material decomposition (MD) image and normalized to that in the psoas muscle to obtain normalized iodine density (NID). The difference in ID and NID between AP and VP was calculated. RESULTS: The ID and NID values of the low grade cancer group were, 14.65 ± 3.38 mg/mL and 1.70 ± 0.33 in AP, and 21.90 ± 3.11 mg/mL and 2.05 ± 0.32 in VP, respectively. The ID and NID values for the high grade cancer group were 20.63 ± 3.72 mg/mL and 2.95 ± 0.72 in AP, and 26.27 ± 3.10mg/mL and 3.51 ± 1.12 in VP, respectively. There was significant difference for ID and NID between the low grade and high grade cancer groups in both AP and VP (all p<0.001). ROC analysis indicated that NID of 1.92 in AP provided 70.3% sensitivity and 97.7% specificity in differentiating low grade cancer from high grade cancer. CONCLUSIONS: The quantitative measurement of iodine density in AP and VP can provide useful information to differentiate low grade colorectal cancer from high grade colorectal cancer with NID in AP providing the greatest diagnostic value.