Molecular subtypes of breast cancer identified by dynamically enhanced MRI radiomics: the delayed phase cannot be ignored.

OBJECTIVES: To compare the diagnostic performance of intratumoral and peritumoral features from different contrast phases of breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building radiomics models for differentiating molecular subtypes of breast cancer. METHODS: This retrospective study included 377 patients with pathologically confirmed breast cancer. Patients were divided into training set (n = 202), validation set (n = 87) and test set (n = 88). The intratumoral volume of interest (VOI) and peritumoral VOI were delineated on primary breast cancers at three different DCE-MRI contrast phases: early, peak, and delayed. Radiomics features were extracted from each phase. After feature standardization, the training set was filtered by variance analysis, correlation analysis, and least absolute shrinkage and selection (LASSO). Using the extracted features, a logistic regression model based on each tumor subtype (Luminal A, Luminal B, HER2-enriched, triple-negative) was established. Ten models based on intratumoral or/plus peritumoral features from three different phases were developed for each differentiation. RESULTS: Radiomics features extracted from delayed phase DCE-MRI demonstrated dominant diagnostic performance over features from other phases. However, the differences were not statistically significant. In the full fusion model for differentiating different molecular subtypes, the most frequently screened features were those from the delayed phase. According to the Shapley additive explanation (SHAP) method, the most important features were also identified from the delayed phase. CONCLUSIONS: The intratumoral and peritumoral radiomics features from the delayed phase of DCE-MRI can provide additional information for preoperative molecular typing. The delayed phase of DCE-MRI cannot be ignored. CRITICAL RELEVANCE STATEMENT: Radiomics features extracted and radiomics models constructed from the delayed phase of DCE-MRI played a crucial role in molecular subtype classification, although no significant difference was observed in the test cohort. KEY POINTS: The molecular subtype of breast cancer provides a basis for setting treatment strategy and prognosis. The delayed-phase radiomics model outperformed that of early-/peak-phases, but no differently than other phases or combinations. Both intra- and peritumoral radiomics features offer valuable insights for molecular typing.

Development of a human-size magnetic particle imaging device for sentinel lymph node biopsy of breast cancer.

In this study, a novel human-size handheld magnetic particle imaging (MPI) system was developed for the high-precision detection of sentinel lymph nodes for breast cancer. The system consisted of a highly sensitive home-made MPI detection probe, a set of concentric coils pair for spatialization, a solenoid coil for uniform excitation at 8 kHz@1.5 mT, and a full mirrored coil set positioned far away from the scanning area. The mirrored coils formed an extremely effective differential pickup structure which suppressed the system noise as high as 100 dB. The different combination of the inner and outer gradient current made the field free point (FFP) move in the Z direction with a uniform intensity of 0.54T/m, while the scanning in the XY direction was implemented mechanically. The third-harmonic signal of the Superparamagnetic Iron Oxide Nanoparticles (SPIONs) at the FFP was detected and then reconstructed synchronously with the current changes. Experiment results showed that the tomographic detection limit was 30 mm in the Z direction, and the sensitivity was about 10 µg Fe SPIONs at 40 mm distance with a spatial resolution of about 5 mm. In the rat experiment, 54 µg intramuscular injected SPIONs were detected successfully in the sentinel lymph node, in which the tracer content was about 1.2% total injected Fe. Additionally, the effective detection time window was confirmed from 4 to 6 min after injection. Relevant clinical ethics are already in the application process. Large mammalian SLNB MPI experiments and 3D preoperative SLNB imaging will be performed in the future.

MRI-based tumor shrinkage patterns after early neoadjuvant therapy in breast cancer: correlation with molecular subtypes and pathological response after therapy.

BACKGROUND: MRI-based tumor shrinkage patterns (TSP) after neoadjuvant therapy (NAT) have been associated with pathological response. However, the understanding of TSP after early NAT remains limited. We aimed to analyze the relationship between TSP after early NAT and pathological response after therapy in different molecular subtypes. METHODS: We prospectively enrolled participants with invasive ductal breast cancers who received NAT and performed pretreatment DCE-MRI from September 2020 to August 2022. Early-stage MRIs were performed after the first (1st-MRI) and/or second (2nd-MRI) cycle of NAT. Tumor shrinkage patterns were categorized into four groups: concentric shrinkage, diffuse decrease (DD), decrease of intensity only (DIO), and stable disease (SD). Logistic regression analysis was performed to identify independent variables associated with pathologic complete response (pCR), and stratified analysis according to tumor hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) disease subtype. RESULTS: 344 participants (mean age: 50 years, 113/345 [33%] pCR) with 345 tumors (1 bilateral) had evaluable 1st-MRI or 2nd-MRI to comprise the primary analysis cohort, of which 244 participants with 245 tumors had evaluable 1st-MRI (82/245 [33%] pCR) and 206 participants with 207 tumors had evaluable 2nd-MRI (69/207 [33%] pCR) to comprise the 1st- and 2nd-timepoint subgroup analysis cohorts, respectively. In the primary analysis, multivariate analysis showed that early DD pattern (OR = 12.08; 95% CI 3.34-43.75; p < 0.001) predicted pCR independently of the change in tumor size (OR = 1.37; 95% CI 0.94-2.01; p = 0.106) in HR+/HER2- subtype, and the change in tumor size was a strong pCR predictor in HER2+ (OR = 1.61; 95% CI 1.22-2.13; p = 0.001) and triple-negative breast cancer (TNBC, OR = 1.61; 95% CI 1.22-2.11; p = 0.001). Compared with the change in tumor size, the SD pattern achieved a higher negative predictive value in HER2+ and TNBC. The statistical significance of complete 1st-timepoint subgroup analysis was consistent with the primary analysis. CONCLUSION: The diffuse decrease pattern in HR+/HER2- subtype and stable disease in HER2+ and TNBC after early NAT could serve as additional straightforward and comprehensible indicators of treatment response. TRIAL REGISTRATION: Trial registration at https://www.chictr.org.cn/ . REGISTRATION NUMBER: ChiCTR2000038578, registered September 24, 2020.

SwinHR: Hemodynamic-powered hierarchical vision transformer for breast tumor segmentation.

Accurate and automated segmentation of breast tumors in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a critical role in computer-aided diagnosis and treatment of breast cancer. However, this task is challenging, due to random variation in tumor sizes, shapes, appearances, and blurred boundaries of tumors caused by inherent heterogeneity of breast cancer. Moreover, the presence of ill-posed artifacts in DCE-MRI further complicate the process of tumor region annotation. To address the challenges above, we propose a scheme (named SwinHR) integrating prior DCE-MRI knowledge and temporal-spatial information of breast tumors. The prior DCE-MRI knowledge refers to hemodynamic information extracted from multiple DCE-MRI phases, which can provide pharmacokinetics information to describe metabolic changes of the tumor cells over the scanning time. The Swin Transformer with hierarchical re-parameterization large kernel architecture (H-RLK) can capture long-range dependencies within DCE-MRI while maintaining computational efficiency by a shifted window-based self-attention mechanism. The use of H-RLK can extract high-level features with a wider receptive field, which can make the model capture contextual information at different levels of abstraction. Extensive experiments are conducted in large-scale datasets to validate the effectiveness of our proposed SwinHR scheme, demonstrating its superiority over recent state-of-the-art segmentation methods. Also, a subgroup analysis split by MRI scanners, field strength, and tumor size is conducted to verify its generalization. The source code is released on (https://github.com/GDPHMediaLab/SwinHR).

Correlation between synthetic MRI relaxometry and apparent diffusion coefficient in breast cancer subtypes with different neoadjuvant therapy response.

BACKGROUND: To evaluate the correlation between synthetic MRI (syMRI) relaxometry and apparent diffusion coefficient (ADC) maps in different breast cancer subtypes and treatment response subgroups. METHODS: Two hundred sixty-three neoadjuvant therapy (NAT)-treated breast cancer patients with baseline MRI were enrolled. Tumor annotations were obtained by drawing regions of interest (ROIs) along the lesion on T1/T2/PD and ADC maps respectively. Histogram features from T1/T2/PD and ADC maps were respectively calculated, and the correlation between each pair of identical features was analyzed. Meanwhile, features between different NAT treatment response groups were compared, and their discriminatory power was evaluated. RESULTS: Among all patients, 20 out of 27 pairs of features weakly correlated (r = - 0.13-0.30). For triple-negative breast cancer (TNBC), features from PD map in the pathological complete response (pCR) group (r = 0.60-0.86) showed higher correlation with ADC than that of the non-pCR group (r = 0.30-0.43), and the mean from the ADC and PD maps in the pCR group strongly correlated (r = 0.86). For HER2-positive, few correlations were found both in the pCR and non-pCR groups. For luminal HER2-negative, T2 map correlated more with ADC than T1 and PD maps. Significant differences were seen in T2 low percentiles and median in the luminal-HER2 negative subtype, yielding moderate AUCs (0.68/0.72/0.71). CONCLUSIONS: The relationship between ADC and PD maps in TNBC may indicate different NAT responses. The no-to-weak correlation between the ADC and syMRI suggests their complementary roles in tumor microenvironment evaluation. CRITICAL RELEVANCE STATEMENT: The relationship between ADC and PD maps in TNBC may indicate different NAT responses, and the no-to-weak correlation between the ADC and syMRI suggests their complementary roles in tumor microenvironment evaluation. KEY POINTS: • The relationship between ADC and PD in TNBC indicates different NAT responses. • The no-to-weak correlations between ADC and syMRI complementarily evaluate tumor microenvironment. • T2 low percentiles and median predict NAT response in luminal-HER2-negative subtype.

[Revealing the Effect of Saline Water Drip Irrigation on Soil Microorganisms in Cotton Fields Based on Metagenomics].

Saline water irrigation has become an important means to alleviate the shortage of freshwater in arid areas. However, long-term saline water irrigation can cause soil salinity accumulation, affect soil microbial community structure, and then affect soil nutrient transformation. In this study, we used metagenomics to investigate the effects of long-term saline water drip irrigation on soil microbial community structure in a cotton field. In the experiment, the salinity of irrigation water (ECw) was set to two treatments:0.35 dS·m-1 and 8.04 dS·m-1 (denoted as FW and SW, respectively), and the nitrogen application rates were 0 kg·hm-2and 360 kg·hm-2 (denoted as N0 and N360, respectively). The results showed that saline water irrigation increased soil water content, salinity, organic carbon, and total nitrogen content and decreased soil pH and available potassium content. Nitrogen fertilizer application increased soil organic carbon, salinity, and total nitrogen content and decreased soil water content, pH, and available potassium content. The dominant bacterial phyla in each treatment were:Proteobacteria, Actinobacteria, Acidobacteria, Chloroflexi, and Gemmatimonadetes. Saline water irrigation significantly increased the relative abundances of Actinobacteria, Chloroflexi, Gemmatimonadetes, and Firmicutes but significantly decreased the relative abundances of Proteobacteria, Acidobacteria, Cyanobacteria, and Nitrospira. Nitrogen fertilizer application significantly increased the relative abundances of Chloroflexi and Nitrospira but significantly decreased the relative abundances of Acidobacteria, Gemmatimonadetes, Planctomycetes, Cyanobacteria, and Verrucomicrobia. LEfSe analysis showed that saline water irrigation had no significant effect on the number of potential biomarkers, and nitrogen fertilizer application decreased the number of potential biomarkers in soil microbial communities. The correlation network diagram showed that the 20 genera had different degrees of correlation, including 44 positive correlations and 48 negative correlations. The core species in the network diagram were Nocardioides, Streptomyces, Pyrinomonas, Candidatus_Solibacter, and Bradyrhizobium spp. Saline water irrigation increased the relative abundances of the denitrification genes nirK, nirS, nasB, and norC and decreased the relative abundances of the nitrification genes amoB, amoC, and nxrA, whereas nitrogen fertilizer application increased the relative abundances of the nitrification genes amoA, amoB, amoC, hao, and nxrA and decreased the relative abundances of the denitrifying genes narB, napA, nasA, and nosZ. Saline water irrigation could adversely affect soil physicochemical properties; SWC, EC1:5, and BD were the main driving factors affecting soil microbial community structure and function genes; and soil microorganisms adapted to soil salt stress by regulating species composition.

Potential Antihuman Epidermal Growth Factor Receptor 2 Target Therapy Beneficiaries: The Role of MRI-Based Radiomics in Distinguishing Human Epidermal Growth Factor Receptor 2-Low Status of Breast Cancer.

BACKGROUND: Multiparametric MRI radiomics could distinguish human epidermal growth factor receptor 2 (HER2)-positive from HER2-negative breast cancers. However, its value for further distinguishing HER2-low from HER2-negative breast cancers has not been investigated. PURPOSE: To investigate whether multiparametric MRI-based radiomics can distinguish HER2-positive from HER2-negative breast cancers (task 1) and HER2-low from HER2-negative breast cancers (task 2). STUDY TYPE: Retrospective. POPULATION: Task 1: 310 operable breast cancer patients from center 1 (97 HER2-positive and 213 HER2-negative); task 2: 213 HER2-negative patients (108 HER2-low and 105 HER2-zero); 59 patients from center 2 (16 HER2-positive, 27 HER2-low and 16 HER2-zero) for external validation. FIELD STRENGTH/SEQUENCE: A 3.0 T/T1-weighted contrast-enhanced imaging (T1CE), diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC). ASSESSMENT: Patients in center 1 were assigned to a training and internal validation cohort at a 2:1 ratio. Intratumoral and peritumoral features were extracted from T1CE and ADC. After dimensionality reduction, the radiomics signatures (RS) of two tasks were developed using features from T1CE (RS-T1CE), ADC (RS-ADC) alone and T1CE + ADC combination (RS-Com). STATISTICAL TESTS: Mann-Whitney U tests, the least absolute shrinkage and selection operator, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). RESULTS: For task 1, RS-ADC yielded higher area under the ROC curve (AUC) in the training, internal, and external validation of 0.767/0.725/0.746 than RS-T1CE (AUC = 0.733/0.674/0.641). For task 2, RS-T1CE yielded higher AUC of 0.765/0.755/0.678 than RS-ADC (AUC = 0.706/0.608/0.630). For both of task 1 and task 2, RS-Com achieved the best performance with AUC of 0.793/0.778/0.760 and 0.820/0.776/0.711, respectively, and obtained higher clinical benefit in DCA compared with RS-T1CE and RS-ADC. The calibration curves of all RS demonstrated a good fitness. DATA CONCLUSION: Multiparametric MRI radiomics could noninvasively and robustly distinguish HER2-positive from HER2-negative breast cancers and further distinguish HER2-low from HER2-negative breast cancers. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

A delta-radiomic lymph node model using dynamic contrast enhanced MRI for the early prediction of axillary response after neoadjuvant chemotherapy in breast cancer patients.

BACKGROUND: The objective of this paper is to explore the value of a delta-radiomic model of the axillary lymph node (ALN) using dynamic contrast-enhanced (DCE) MRI for early prediction of the axillary pathological complete response (pCR) of breast cancer patients after neoadjuvant chemotherapy (NAC). METHODS: A total of 120 patients with ALN-positive breast cancer who underwent breast MRI before and after the first cycle of NAC between October 2018 and May 2021 were prospectively included in this study. Patients were divided into a training (n = 84) and validation (n = 36) cohort based on the temporal order of their treatments. Radiomic features were extracted from the largest slice of targeted ALN on DCE-MRI at pretreatment and after one cycle of NAC, and their changes (delta-) were calculated and recorded. Logistic regression was then applied to build radiomic models using the pretreatment (pre-), first-cycle(1st-), and changes (delta-) radiomic features separately. A clinical model was also built and combined with the radiomic models. The models were evaluated by discrimination, calibration, and clinical application and compared using DeLong test. RESULTS: Among the three radiomic models, the ALN delta-radiomic model performed the best with AUCs of 0.851 (95% CI: 0.770-0.932) and 0.822 (95% CI: 0.685-0.958) in the training and validation cohorts, respectively. The clinical model yielded moderate AUCs of 0.742 (95% CI: 0.637-0.846) and 0.723 (95% CI: 0.550-0.896), respectively. After combining clinical features to the delta-radiomics model, the efficacy of the combined model (AUC = 0.932) in the training cohort was significantly higher than that of both the delta-radiomic model (Delong p = 0.017) and the clinical model (Delong p < 0.001) individually. Additionally, in the validation cohort, the combined model had the highest AUC (0.859) of any of the models we tested although this was not statistically different from any other individual model's validation AUC. Calibration and decision curves showed a good agreement and a high clinical benefit for the combined model. CONCLUSION: This preliminary study indicates that ALN-based delta-radiomic model combined with clinical features is a promising strategy for the early prediction of downstaging ALN status after NAC. Future axillary MRI applications need to be further explored.

Time Course Changes of Synthetic Relaxation Time During Neoadjuvant Chemotherapy in Breast Cancer: The Optimal Parameter for Treatment Response Evaluation.

BACKGROUND: Synthetic MRI (syMRI) has enabled quantification of multiple relaxation parameters (T1/T2 relaxation time [T1/T2], proton density [PD]), and their longitudinal change during neoadjuvant chemotherapy (NAC) promises to be valuable parameters for treatment response evaluation in breast cancer. PURPOSE: To investigate the time course changes of syMRI parameters during NAC and evaluate their value as predictors for pathological complete response (pCR) in breast cancer. STUDY TYPE: Retrospective, longitudinal. POPULATION: A total of 129 women (median age, 50 years; range, 28-69 years) with locally advanced breast cancer who underwent NAC; all performed multiple conventional breast MRI examinations with added syMRI during NAC. FIELD STRENGTH/SEQUENCE: A 3.0 T, T1-weighted dynamic contrast enhanced and syMRI acquired by a multiple-dynamic, multiple-echo sequence. ASSESSMENT: Breast MRI was set at four time-points: baseline, after one cycle, after three or four cycles of NAC and preoperation. SyMRI parameters and tumor diameters were measured and their changes from baseline were calculated. All parameters were compared between pCR and non-pCR. Interaction between syMRI parameters and clinicopathological features was analyzed. STATISTICAL TESTS: Mann-Whitney U tests, random effects model of repeated measurement, receiver operating characteristic (ROC) analysis, interaction analysis. RESULTS: Median synthetic T1/T2/PD and tumor diameter generally decreased throughout NAC. Absolute T1 at early-NAC, T1, and PD at mid-NAC were significantly lower in the pCR group. After early-NAC, the T1 change was significantly higher in the pCR (median ± IQR, 18.17 ± 11.33) than the non-pCR group (median ± IQR, 10.90 ± 10.03), with the highest area under the ROC curves (AUC) of 0.769 (95% CI, 0.684-0.838). Interaction analysis showed that histological grade III patients had higher odds ratio (OR) (OR = 1.206) compared to grade II patients (OR = 1.067). DATA CONCLUSION: Synthetic T1 changes after one cycle of NAC maybe useful for early evaluating NAC response in breast cancer during whole treatment cycles. However, its discriminative ability is significantly affected by histological grade. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI Predicts Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy.

OBJECTIVE: To investigate the value of delta-radiomics after the first cycle of neoadjuvant chemotherapy (NAC) using dynamic contrast-enhanced (DCE) MRI for early prediction of pathological complete response (pCR) in patients with breast cancer. METHODS: From September 2018 to May 2021, a total of 140 consecutive patients (training, n = 98: validation, n = 42), newly diagnosed with breast cancer who received NAC before surgery, were prospectively enrolled. All patients underwent DCE-MRI at pre-NAC (pre-) and after the first cycle (1st-) of NAC. Radiomic features were extracted from the postcontrast early, peak, and delay phases. Delta-radiomics features were computed in each contrast phases. Least absolute shrinkage and selection operator (LASSO) and a logistic regression model were used to select features and build models. The model performance was assessed by receiver operating characteristic (ROC) analysis and compared by DeLong test. RESULTS: The delta-radiomics model based on the early phases of DCE-MRI showed a highest AUC (0.917/0.842 for training/validation cohort) compared with that using the peak and delay phases images. The delta-radiomics model outperformed the pre-radiomics model (AUC = 0.759/0.617, p = 0.011/0.047 for training/validation cohort) in early phase. Based on the optimal model, longitudinal fusion radiomic models achieved an AUC of 0.871/0.869 in training/validation cohort. Clinical-radiomics model generated good calibration and discrimination capacity with AUC 0.934 (95%CI: 0.882, 0.986)/0.864 (95%CI: 0.746, 0.982) for training and validation cohort. Delta-radiomics based on early contrast phases of DCE-MRI combined clinicopathology information could predict pCR after one cycle of NAC in patients with breast cancer.

Contrast-free MRI quantitative parameters for early prediction of pathological response to neoadjuvant chemotherapy in breast cancer.

OBJECTIVES: To assess early changes in synthetic relaxometry after neoadjuvant chemotherapy (NAC) for breast cancer and establish a model with contrast-free quantitative parameters for early prediction of pathological response. METHODS: From March 2019 to January 2021, breast MRI were performed for a primary cohort of women with breast cancer before (n = 102) and after the first (n = 93) and second (n = 90) cycle of NAC. Tumor size, synthetic relaxometry (T1/T2 relaxation time [T1/T2], proton density), and ADC were obtained, and the changes after treatment were calculated. Prediction models were established by multivariate logistic regression; evaluated with discrimination, calibration, and clinical application; and compared with Delong tests, net reclassification (NRI), and integrated discrimination index (IDI). External validation was performed from February to June 2021 with an independent cohort of 35 patients. RESULTS: In the primary cohort, all parameters changed after early treatment. Synthetic relaxometry decreased to a greater degree in major histologic responders (MHR, Miller-Payne G4-5) compared with non-MHR (Miller-Payne G1-3). A model combining ADC after treatment, changes in T1 and tumor size, and cancer subtype achieved the highest AUC after the first (primary/validation cohort, 0.83/0.82) and second cycles (primary/validation cohort, 0.85/0.84). No difference of AUC (p ≥ 0.27), NRI (p ≥ 0.31), and IDI (p ≥ 0.32) was found between models with different cycles and size-measured sequences. Model calibration and decision curves demonstrated a good fitness and clinical benefit, respectively. CONCLUSIONS: Early reduction in synthetic relaxometry indicated pathological response to NAC. Contrast-free T1 and ADC combined with size and cancer subtype predicted effectively pathological response after one NAC cycle. KEY POINTS: • Synthetic MRI relaxometry changed after early neoadjuvant chemotherapy, which demonstrated pathological response for mass-like breast cancers. • Contrast-free quantitative parameters including T1 relaxation time and apparent diffusion coefficient, combined with tumor size and cancer subtype, stratified major histologic responders. • A contrast-free model predicted an early pathological response after the first treatment cycle of neoadjuvant chemotherapy.

Interspecies Interactions of the 2,6-Dichlorobenzamide Degrading Aminobacter sp. MSH1 with Resident Sand Filter Bacteria: Indications for Mutual Cooperative Interactions That Improve BAM Mineralization Activity.

Bioaugmentation often involves an invasion process requiring the establishment and activity of a foreign microbe in the resident community of the target environment. Interactions with resident micro-organisms, either antagonistic or cooperative, are believed to impact invasion. However, few studies have examined the variability of interactions between an invader and resident species of its target environment, and none of them considered a bioremediation context. Aminobacter sp. MSH1 mineralizing the groundwater micropollutant 2,6-dichlorobenzamide (BAM), is proposed for bioaugmentation of sand filters used in drinking water production to avert BAM contamination. We examined the nature of the interactions between MSH1 and 13 sand filter resident bacteria in dual and triple species assemblies in sand microcosms. The residents affected MSH1-mediated BAM mineralization without always impacting MSH1 cell densities, indicating effects on cell physiology rather than on cell number. Exploitative competition explained most of the effects (70%), but indications of interference competition were also found. Two residents improved BAM mineralization in dual species assemblies, apparently in a mutual cooperation, and overruled negative effects by others in triple species systems. The results suggest that sand filter communities contain species that increase MSH1 fitness. This opens doors for assisting bioaugmentation through co-inoculation with "helper" bacteria originating from and adapted to the target environment.

New Helvolic Acid Derivatives with Antibacterial Activities from Sarocladium oryzae DX-THL3, an Endophytic Fungus from Dongxiang Wild Rice (Oryza rufipogon Griff.).

Three new helvolic acid derivatives (named sarocladilactone A (1), sarocladilactone B (2) and sarocladic acid A (3a)), together with five known compounds (6,16-diacetoxy-25-hy- droxy-3,7-dioxy-29-nordammara-1,17(20)-dien-21-oic acid (3b), helvolic acid (4), helvolinic acid (5), 6-desacetoxy-helvolic acid (6) and 1,2-dihydrohelvolic acid (7)), were isolated from the endophytic fungus DX-THL3, obtained from the leaf of Dongxiang wild rice (Oryza rufipogon Griff.). The structures of the new compounds were elucidated via HR-MS, extensive 1D and 2D NMR analysis and comparison with reported data. Compounds 1, 2, 4, 5, 6 and 7 exhibited potent antibacterial activities. In particular, sarocladilactone B (2), helvolinic acid (5) and 6-desacetoxy-helvolic acid (6) exhibited strongly Staphylococcus aureus inhibitory activity with minimum inhibitory concentration (MIC) values of 4, 1 and 4 µg/mL, respectively. The structure-activity relationship (SAR) of these compounds was primarily summarized.

Improved discrimination of molecular subtypes in invasive breast cancer: Comparison of multiple quantitative parameters from breast MRI.

PURPOSE: To compare multiple quantitative parameters from breast magnetic resonance imaging (MRI) with the synthetic MRI sequence included for discrimination of molecular subtypes of invasive breast cancer. MATERIALS AND METHODS: Between March 2019 and September 2020, two hundred breast cancer patients underwent preoperative breast multiparametric MRI examinations including synthetic MRI, diffusion weighted imaging (DWI) and dynamic contrast enhancement (DCE)-MRI sequences. MRI morphological features, T1 and T2 relaxation times (T1, T2) and proton density (PD) values from synthetic MRI, Ktrans, Kep, and Ve from DCE-MRI, mean apparent diffusion coefficient (ADC) from DWI and tumor volume were measured. Quantitative parameters were compared according to molecular markers and subtypes. Logistic regression were performed to find the related MRI parameters and establish combined parameters. The comparison between single and combined quantitative parameters by using DeLong tests. RESULTS: T1, T2 values were significantly higher in hormone receptor (HR)- negative and Ki67 > 14% tumors (p < 0.05). Human epidermal growth factor receptor 2 (HER2)-positive tumors demonstrated significantly higher Ktrans and Kep (p < 0.01). Mean ADC values were significantly decreased in HR-positive and Ki67 > 14% tumors (p < 0.01). Tumor volumes were significantly higher in HER2-positive and Ki67 > 14% tumors (p < 0.05). Independent influencing factors were lower T2 values (p < 0.001), smaller tumor volume (p = 0.031) and higher mean ADC (p = 0.002) associated with luminal A subtype, while T1 values (p = 0.007) was the only quantitative parameter associated with triple-negative subtype. The diagnostic efficiency of combined parameters (T2 + mean ADC + volume) (AUC = 0.765) was significantly higher than that of mean ADC (AUC = 0.666, p = 0.031 by DeLong test) and volume (AUC = 0.650, p = 0.008 by DeLong test) for separating luminal A subtype. CONCLUSIONS: MRI quantitative parameters could help distinguish molecular markers and subtypes. The emerging synthetic MRI parameters - T1 values were associated with the TN subtype, and combined parameters with added T2 values might improve the discrimination of the luminal A subtype. Application of synthetic MRI can enrich quantitative descriptors from breast MRI.

A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has emerged as a global pandemic. According to the diagnosis and treatment guidelines of China, negative reverse transcription-polymerase chain reaction (RT-PCR) is the key criterion for discharging COVID-19 patients. However, repeated RT-PCR tests lead to medical waste and prolonged hospital stays for COVID-19 patients during the recovery period. Our purpose is to assess a model based on chest computed tomography (CT) radiomic features and clinical characteristics to predict RT-PCR negativity during clinical treatment. METHODS: From February 10 to March 10, 2020, 203 mild COVID-19 patients in Fangcang Shelter Hospital were retrospectively included (training: n = 141; testing: n = 62), and clinical characteristics were collected. Lung abnormalities on chest CT images were segmented with a deep learning algorithm. CT quantitative features and radiomic features were automatically extracted. Clinical characteristics and CT quantitative features were compared between RT-PCR-negative and RT-PCR-positive groups. Univariate logistic regression and Spearman correlation analyses identified the strongest features associated with RT-PCR negativity, and a multivariate logistic regression model was established. The diagnostic performance was evaluated for both cohorts. RESULTS: The RT-PCR-negative group had a longer time interval from symptom onset to CT exams than the RT-PCR-positive group (median 23 vs. 16 days, p < 0.001). There was no significant difference in the other clinical characteristics or CT quantitative features. In addition to the time interval from symptom onset to CT exams, nine CT radiomic features were selected for the model. ROC curve analysis revealed AUCs of 0.811 and 0.812 for differentiating the RT-PCR-negative group, with sensitivity/specificity of 0.765/0.625 and 0.784/0.600 in the training and testing datasets, respectively. CONCLUSION: The model combining CT radiomic features and clinical data helped predict RT-PCR negativity during clinical treatment, indicating the proper time for RT-PCR retesting.

Chest lesion CT radiological features and quantitative analysis in RT-PCR turned negative and clinical symptoms resolved COVID-19 patients.

BACKGROUND: Many studies have described lung lesion computed tomography (CT) features of coronavirus disease 2019 (COVID-19) patients at the early and progressive stages. In this study, we aim to evaluate lung lesion CT radiological features along with quantitative analysis for the COVID-19 patients ready for discharge. METHODS: From February 10 to March 10, 2020, 125 COVID-19 patients (age: 16-67 years, 63 males) ready for discharge, with two consecutive negative reverse transcription-polymerase chain reaction (RT-PCR) and no clinical symptoms for more than 3 days, were included. The pre-discharge CT was performed on all patients 1-3 days after the second negative RT-PCR test, and the follow-up CTs were performed on 44 patients 2-13 days later. The imaging features and quantitative analysis were evaluated on both the pre-discharge and the follow-up CTs, by both radiologists and an artificial intelligence (AI) software. RESULTS: On the pre-discharge CT, the most common CT findings included ground-glass opacity (GGO) (99/125, 79.2%) with bilateral mixed distribution, and fibrosis (56/125, 44.8%) with bilateral subpleural distribution. Enlarged mediastinal lymph nodes were also commonly observed (45/125, 36.0%). AI enabled quantitative analysis showed the right lower lobe was mostly involved, and lesions most commonly had CT value of -570 to -470 HU consistent with GGO. Follow-up CT showed GGO decrease in size and density (40/40, 100%) and fibrosis reduction (17/26, 65.4%). Compared with the pre-discharge CT results, quantitative analysis shows the lung lesion volume regressed significantly at follow-up. CONCLUSIONS: For COVID-19 patients ready for discharge, GGO and fibrosis are the main CT features and they further regress at follow-up.

Value of integrated PET-IVIM MR in assessing metastases in hypermetabolic pelvic lymph nodes in cervical cancer: a multi-parameter study.

PURPOSE: To evaluate the value of integrated multi-parameter positron emission tomography-intravoxel incoherent motion magnetic resonance (PET-IVIM MR) imaging for pelvic lymph nodes with high FDG uptake in cervical cancer, and to determine the best combination of parameters. METHODS: A total of 38 patients with 59 lymph nodes with high FDG uptake were included. The imaging parameters of the lymph nodes were calculated by PET-IVIM MR, and the differences between lymph nodes diagnosed by postoperative pathology as metastasis versus non-metastasis were compared. We used the receiver operating characteristic (ROC) curve and logistic regression to construct a combination prediction model to filter low value and similar parameters, in order to search the optimal combination of PET/MR parameters for predicting pathologically confirmed metastatic lymph nodes. The correlation between diffusion parameters and metabolic parameters was analyzed by Spearman's rank correlation. RESULTS: The maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), total metabolic tumor volume (MTV), total lesion glycolysis (TLG), apparent diffusion coefficient (ADC), diffusion-related coefficient (D), and perfusion-related parameter (F) showed significant differences between the metastatic and non-metastatic groups (p < 0.05). The combination of MTV, SUVmax, and D had the strongest predictive value (area under the ROC 0.983, p < 0.05). SUVmax, SUVmean, and TLG weakly correlated with F (R = - 0.306, - 0.290, and - 0.310; p < 0.05). CONCLUSIONS: The combination of MTV, SUVmax, and D may have a better diagnostic performance than PET- or IVIM-derived parameters either in combination or individually. No strong correlation exists between diffusion parameters and metabolic parameters. KEY POINTS: • Integrated PET-IVIM MR may assist to characterize lymph node status. • The combination of MTV, SUVmax, and D may have a better diagnostic performance than PET- or IVIM-derived parameters either in combination or individually for the assessment of pelvic lymph nodes with high FDG uptake. • No strong correlation exists between diffusion parameters and metabolic parameters in pelvic lymph nodes with high FDG uptake.

Multiparametric PET/MR (PET and MR-IVIM) for the evaluation of early treatment response and prediction of tumor recurrence in patients with locally advanced cervical cancer.

OBJECTIVES: To assess the value of 18F-FDG PET and MR-IVIM parameters before and during concurrent chemoradiotherapy (CCRT) for evaluating early treatment response and predicting tumor recurrence in patients with locally advanced cervical cancer (LACC) using a hybrid PET/MR scanner. METHODS: Fifty-one patients with LACC underwent pelvic PET/MR scans with an IVIM sequence at two time-points (pretreatment [pre] and midtreatment [mid]). Pre- and mid-PET parameters (SUVmax, MTV, TLG) and IVIM parameters (D, F, D*) and their percentage changes (Δ%SUVmax, Δ%MTV, Δ%TLG, Δ%D, Δ%F, Δ%D*) were calculated. We selected independent imaging parameters and built a combined prediction model incorporating imaging parameters and clinicopathological risk factors. The performance of the combinative evaluation for tumor early shrinkage rates (TESR) and the prediction model for tumor recurrence was assessed. RESULTS: Thirty-two patients were classified into the good response (GR) group with TESR ≥ 50%, and 19 patients were categorized into the poor response (PR) group with TESR < 50%. Δ%D (p = 0.013) and Δ%F (p = 0.006) are independently related to TESR with superior combined diagnostic ability (AUC = 0.901). Pre-TLG, Δ%D, and suspicious lymph node metastasis (SLNM) were selected for the construction of the combined prediction model. The model for identifying the patients with high risk of tumor recurrence reached a moderate predictive ability and good stability with c-index of 0.764 (95% CI, 0.672-0.855). CONCLUSION: The combined prediction model based on pretreatment PET metabolic parameter (pre-TLG), IVIM-D percentage changes, and LNs status provides great potential to identify the LACC patients with high risk of recurrence at early stage of CCRT. KEY POINTS: • PET/MR plus IVIM offers various complementary information for LACC. • IVIM-D and IVIM-F percentage changes are independently related to tumor early shrinkage rates. • The combined prediction model can help identify the LACC patients with high risk of tumor recurrence.

Early treatment response of patients undergoing concurrent chemoradiotherapy for cervical cancer: An evaluation of integrated multi-parameter PET-IVIM MR.

PURPOSE: To evaluate the value of integrated multi-parameter positron emission tomography-intravoxel incoherent motion magnetic resonance (PET-IVIM MR) imaging in the assessment of treatment response in patients with cervical cancer treated with concurrent chemoradiotherapy (CCRT). MATERIALS AND METHODS: A total of 41 patients underwent PET-MR scans before, during (at the end of the third week) and after CCRT were included in this study. The PET-MR imaging parameters were measured on the tumor and the percentage change between the two time points were calculated before and during the treatment. These parameters were used to evaluate treatment response. A combination prediction model was constructed using multivariate logistic regression. European Organization for Research and Treatment of Cancer (EORTC)criteria, measured by the post-therapy(PostTx) PET/MR were used to classify treatment responses: patients were classified into the complete metabolic responder (CMR) group or the non-complete metabolic responders (N-CMR) group. The correlations between PET and IVIM parameters and percentage changes during CCRT were investigated using the Spearman rank correlation. RESULTS: In all, 13 of 41 (31.7%) patients were defined as N-CMR group. According to constructing multivariate logistic regression, the combination of pre-therapy(Pre-Tx) total metabolic tumor volume(MTV), the percentage changes of the maximum standardized uptake value (ΔSUVmax) and the mean diffusion-related coefficient (ΔDmean) during CCRT had the strongest predictive potentiality(AUC 0.912, P < 0.05).In addition,during CCRT the percentage changes in minimum diffusion-related coefficient(ΔDmin) was correlated with ΔSUVmax(Spearman correlation coefficient, r＝0.338, P < 0.05). CONCLUSIONS: The combination of Pre-Tx MTV,ΔSUVmax and ΔDmean had the strongest predictive value in evaluating treatment response for patients with cervical cancer treated with CCRT. Other imaging parameters can be replaced by these 3 parameters because of their similarities and lower predictive values. In addition, ΔDmin and ΔSUVmax have a similar value for evaluating treatment response after CCRT in cervical cancer.