Preoperative MRI features predict failed breast-conserving surgery: construction of a predictive model.

Background: Breast-conserving surgery (BCS) is the preferred method for early breast cancer, and the accurate preoperative prediction of the feasibility of BCS can formulate the surgical plan and reduce the violation of the patient's will. The present study proposed to explore the preoperative magnetic resonance imaging (MRI) features associated with failed BCS and constructed an MRI-based model to predict BCS. Methods: This retrospective study included patients between March 2015 and July 2016, who planned to undergo BCS, had preoperative MRI examination, and had at least 2 years of follow-up. A total of 30 patients with failed BCS were identified and matched with 90 patients with successful BCS (ratio 1:3) according to age, neoadjuvant therapy, and hormone receptor expression. The patients were divided into the training group for model construction and the testing group for model validation. The MRI features, including the site of the tumor, the lesion type, and the lesion and breast volume, were compared between failure and successful BCS groups. A multivariate logistic model for predicting failed BCS was constructed using independent factors associated with failed BCS from the training group and was evaluated in the testing group. The performance of the model was evaluated using the receiver operating characteristic (ROC) curve. Results: The mean age of the cohort was 45.7±10.3 years. A significantly more non-mass lesion and multifocality, the larger volume of lesion, and the ratio of lesion and breast volume were observed in failed BCS group compared to the successful BCS group. The ratio of lesion and breast volume and multifocality were independent factors associated with failed BCS, odds ratios were 1.044 (95% CI: 1.016-1.074) and 11.161 (95% CI: 1.739-71.652), respectively. An MRI-based model for predicting failed BCS was established, the area under the ROC curves in the training and testing group were 0.902 and 0.821, respectively. Conclusions: This model might help clinicians predict failed BCS preoperatively and make an accurate surgical strategy.

Deep learning model improves radiologists' performance in detection and classification of breast lesions.

OBJECTIVE: Computer-aided diagnosis using deep learning algorithms has been initially applied in the field of mammography, but there is no large-scale clinical application. METHODS: This study proposed to develop and verify an artificial intelligence model based on mammography. Firstly, mammograms retrospectively collected from six centers were randomized to a training dataset and a validation dataset for establishing the model. Secondly, the model was tested by comparing 12 radiologists' performance with and without it. Finally, prospectively enrolled women with mammograms from six centers were diagnosed by radiologists with the model. The detection and diagnostic capabilities were evaluated using the free-response receiver operating characteristic (FROC) curve and ROC curve. RESULTS: The sensitivity of model for detecting lesions after matching was 0.908 for false positive rate of 0.25 in unilateral images. The area under ROC curve (AUC) to distinguish the benign lesions from malignant lesions was 0.855 [95% confidence interval (95% CI): 0.830, 0.880]. The performance of 12 radiologists with the model was higher than that of radiologists alone (AUC: 0.852 vs. 0.805, P=0.005). The mean reading time of with the model was shorter than that of reading alone (80.18 s vs. 62.28 s, P=0.032). In prospective application, the sensitivity of detection reached 0.887 at false positive rate of 0.25; the AUC of radiologists with the model was 0.983 (95% CI: 0.978, 0.988), with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 94.36%, 98.07%, 87.76%, and 99.09%, respectively. CONCLUSIONS: The artificial intelligence model exhibits high accuracy for detecting and diagnosing breast lesions, improves diagnostic accuracy and saves time.

Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method.

BACKGROUND: The aim of the study was to develop a deep learning (DL) algorithm to evaluate the pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer. METHODS: A total of 302 breast cancer patients in this retrospective study were randomly divided into a training set (n = 244) and a validation set (n = 58). Tumor regions were manually delineated on each slice by two expert radiologists on enhanced T1-weighted images. Pathological results were used as ground truth. Deep learning network contained five repetitions of convolution and max-pooling layers and ended with three dense layers. The pre-NAC model and post-NAC model inputted six phases of pre-NAC and post-NAC images, respectively. The combined model used 12 channels from six phases of pre-NAC and six phases of post-NAC images. All models above included three indexes of molecular type as one additional input channel. RESULTS: The training set contained 137 non-pCR and 107 pCR participants. The validation set contained 33 non-pCR and 25 pCR participants. The area under the receiver operating characteristic (ROC) curve (AUC) of three models was 0.553 for pre-NAC, 0.968 for post-NAC and 0.970 for the combined data, respectively. A significant difference was found in AUC between using pre-NAC data alone and combined data (P < 0.001). The positive predictive value of the combined model was greater than that of the post-NAC model (100% vs. 82.8%, P = 0.033). CONCLUSION: This study established a deep learning model to predict PCR status after neoadjuvant therapy by combining pre-NAC and post-NAC MRI data. The model performed better than using pre-NAC data only, and also performed better than using post-NAC data only. KEY POINTS: Significant findings of the study. It achieved an AUC of 0.968 for pCR prediction. It showed a significantly greater AUC than using pre-NAC data only. What this study adds This study established a deep learning model to predict PCR status after neoadjuvant therapy by combining pre-NAC and post-NAC MRI data.

MRI evaluation of extramural vascular invasion by inexperienced radiologists.

OBJECTIVE: We proposed to determine whether the performance of inexperienced radiologists in determining extramural vascular invasion (EMVI) in rectal cancer on MRI can be promoted by means of targeted training. METHODS: 230 rectal cancer patients who underwent pre-operative chemoradiotherapy were included. Pre-therapy and post-therapy MR images and pathology EMVI evaluation were available for cases. 230 cases were randomly divided into 150 training cases and 80 testing cases, including 40 testing case A and 40 testing case B. Four radiologists were included for MRI EMVI evaluation, who were divided into targeted training group and non-targeted training group. The two groups evaluated testing case A at baseline, 3 month and 6 month, evaluated testing case B at 6 month. The main outcome was agreement with expert-reference for pre-therapy and post-therapy evaluation, the other outcome was accuracy with pathology for post-therapy evaluation. RESULTS: After 6 months of training, targeted training group showed statistically higher agreement with expert-reference than non-targeted training group for both pre-therapy and post-therapy MRI EMVI evaluation of testing case A and testing case B, all p < 0.05. Targeted training group also showed significantly higher accuracy with pathology than non-targeted training group for post-therapy evaluation of testing case A and testing case B after 6 months of training, all p < 0.05. CONCLUSION: The diagnostic performance for MRI EMVI evaluation could be promoted by targeted training for inexperienced radiologist. ADVANCES IN KNOWLEDGE: This study provided the first evidence that after 6 month targeted training, inexperienced radiologists demonstrated improved diagnostic performance, with a 20% increase in agreement with expert-reference for both pre-therapy and post-therapy MRI EMVI evaluation and also a 20% increase in or accuracy with pathology for post-therapy evaluation, while inexperienced radiologists could not gain obvious improvement in MRI EMVI evaluation through the same period of regular clinical practice. It indicated that targeted training may be necessary for helping inexperienced radiologist to acquire adequate experience for the MRI EMVI evaluation of rectal cancer, especially for radiologist who works in a medical unit where MRI EMVI diagnosis is uncommon.

Lignans from the Twigs of Litsea cubeba and Their Bioactivities.

Litsea cubeba, an important medicinal plant, is widely used as a traditional Chinese medicine and spice. Using cytotoxicity-guided fractionation, nine new lignans 1⁻9 and ten known analogues 10⁻19 were obtained from the EtOH extract of the twigs of L. cubeba. Their structures were assigned by extensive 1D- and 2D-NMR experiments, and the absolute configurations were resolved by specific rotation and a combination of experimental and theoretically calculated electronic circular dichroism (ECD) spectra. In the cytotoxicity assay, 7',9-epoxylignans with feruloyl or cinnamoyl groups (compounds 7⁻9, 13 and 14) were selectively cytotoxic against NCI-H1650 cell line, while the dibenzylbutyrolactone lignans 17⁻19 exerted cytotoxicities against HCT-116 and A2780 cell lines. The results highlighted the structure-activity relationship importance of a feruloyl or a cinnamoyl moiety at C-9' or/and C-7 ketone in 7',9-epoxylignans. Furthermore, compound 11 was moderate active toward protein tyrosine phosphatase 1B (PTP1B) with an IC50 value of 13.5 µM, and compounds 4⁻6, 11 and 12 displayed inhibitory activity against LPS-induced NO production in RAW264.7 macrophages, with IC50 values of 46.8, 50.1, 58.6, 47.5, and 66.5 µM, respectively.

Two new terpenoid ester glycosides from the twigs of Litsea cubeba.

A new sesquiterpenoid ester glycoside (1) and a new monoterpenoid ester glycoside (2) have been isolated from an ethanol extract of the twigs of Litsea cubeba. Their structures were elucidated by extensive 1D- and 2D-NMR experiments, and the absolute configurations were determined by chemical methods, specific rotation, and a combination of experimental and theoretically calculated electronic circular dichroism spectra. Compound 1 exhibited selective cytotoxicity against A549 and HCT-8 cell lines with the IC50 values of 8.9 and 9.6 µM, respectively.

[Water soluble constituents from the twigs of Litsea cubeba].

Twenty five known aromatic glycosides (1-25) and three known sesquiterpene glycosides (26-28) have been isolated from the twigs of Litsea cubeba by using various chromatographic techniques. Their structures were identified by spectroscopic data analysis (MS, IR, 1D and 2D NMR) as (7S,8R)-dehydrodiconiferyl alcohol 4,9'-di-O-ß-D-glucopyranoside(1),(7S,8R)-5-methoxydihydrodehydrodiconiferyl alcohol 4-O-ß-D-glucopyranoside(2), (7S,8R)-urolignoside(3), (7R,8S)-dihydrodehydrodiconiferyl alcohol 4-O-ß-D-glucopyranoside(4), saposide B(5), lanicepside A(6), matairesinol-4-O-ß-D-glucopyranoside (7), tyraxjaponoside B(8), (+)-lyoniresinol-9'-O-ß-D-glucopyranoside (9), alaschanisoside A (10), syringin (11), psoralenoside (12), isopsoralenoside (13), scopolin(14), 2,6-dimethoxy-4-hydroxyphenol-1-O-ß-D-glucopyranoside (15), 3-hydroxy-4,5-dimethoxyphenyl-ß-D-glucopyranoside (16), 2-(3,4-dihydroxyphenyl)ethyl-ß-D-glucopyrnoside (17), 2-(4-dihydroxyphenyl)ethyl-ß-D-glucopyranoside (18), (+)-catechin-7-O-ß-D-glucopyranoside (19), 3'-O-methylepicatechin-7-O-ß-D-glucopyranoside (20), kaempferitrin (21), quercetin-3-O-α-L-rhamnopyranside (22), kaempferol-3-O-ß-D-glucopyranoside (23), kaempferol 3-O-ß-D-glucopyranosyl(1→2)-O-ß-D-galactopyr anoside-7-O-α-L-rhamnopyranoside (24), quercetin 3-O-α-L-rhamnopyranosyl(1→6)-O-ß-D-glucopyranosyl(1→3)-O-α-L-rhamnopyranosyl(1→2)-O-ß-D-glucopyranoside (25), staphylionoside D(26), vomifoliol 9-O-ß-D-glucopyranoside (27), dihydrovomifoliol-O-ß-D-glucopyranoside (28). Compounds 1-21 and 24-28 were obtained from this genus for the first time.

[A new styrene dimer derivative from Litsea greenmaniana].

A new styrene dimer derivative has been isolated from the branch of Litsea greenmaniana by column chromatography over silica gel and Sephadex LH-20, as well as semi-preparative HPLC. Its structure was identified by spectroscopic data analysis (MS, UV, IR, 1D and 2D NMR) as (E)-2,4-bis(p-hydroxyphenyl)-2-butenol, named as listeanol. At a concentration of 1×10-5 mol•L⁻¹, compound 1 was inactive in the assays, including cytotoxicity against human tumor cell lines (HCT-8, Bel-7402, BGC-823, A549 and A2780), antioxidant activity in Fe²âº-cystine-induced rat liver microsomal lipid peroxidation, neuroprotective activity against serum deprivation or glutamate induced neurotoxicity in cultures of PC12 cells, and the inhibitory activity against protein tyrosine phosphatase 1B (PTP1B).

Bioactive Glycosides from the Twigs of Litsea cubeba.

The air-dried twigs of Litsea cubeba, a traditional Chinese medicinal tree, afforded 10 new aromatic glycosides (1-10) and 26 known analogues. Their structures were assigned by extensive 1D and 2D NMR experiments, and the absolute configurations were resolved by chemical methods, electronic circular dichroism, specific rotation, and X-ray crystallographic analysis. Compound 4 is the first example of a naturally occurring homoneolignan glucoside. Compounds 4, 6-8, and the known neolignan glucosides (11, 12, and 14) at respective 10 µM concentrations were found to reduce acetaminophen-induced HepG2 cell injury with 30.5-46.0% inhibitions. Furthermore, compounds 12 and 15 demonstrated moderate inhibitory activities against HDAC1, with IC50 values of 3.6 and 4.6 µM, respectively.

Comparison of affective and semantic priming in different SOA.

Researchers have been at odds on whether affective or semantic priming is faster or stronger. The present study selects a series of facial expression photos and words, which have definite emotional meaning or gender meaning, to set up experiment including both affective and semantic priming. The intensity of emotion and gender information in the prime as well as the strength of emotional or semantic (in gender) relationship between the prime and the target is matched. Three groups of participants are employed separately in our experiment varied with stimulus onset asynchrony (SOA) as 50, 250 or 500 ms. The results show that the difference between two types of priming effect is revealed when the SOA is at 50 ms, in which the affective priming effect is presented when the prime has negative emotion. It indicates that SOA can affect the comparison between the affective and semantic priming, and the former takes the priority in the automatic processing level.

[Chemical constituents from ethyl acetate exaction of root of Paeonia lactiflora].

Two new phenylpropanoids(1 and 2), together with thirteen known compounds(3-15), have been isolated from the root of Paeonia lactiflora by using various chromatographic techniques. Their structures were identified by spectroscopic data analysis(MS,IR,1D and 2D NMR)as(+)-(7R,8R)-1-guaiacyl-1,2-propanediolacetonide(1),(-)-(7R,8S)-1-guaiacyl-1,2-propanediolacetonide(2),O-senecioyllomatin(3),O-angeloyllomatin(4),(+)-cis-3'-senecioyloxy-4'-angeloyloxy-3',4'-dihydroseselin(5),columbianadin(6), benzyl 2,5-dihydroxybenzoate(7),3,6-dimethyl-5-hydroxyBenzo-furan(8),(S)-evofolin-A(9),2,3-dihydroxy-4-methoxyacetophenone(10), 2,5-dihydroxy-4-methoxyacetophenone(11), 2,5-dihydroxy-4-methyl acetophenone(12),ethyl 4-hydroxybenzoate(13), vanillic acid(14),and 4-hydroxy-3-methoxybenzaldehyde(15).Compounds 1 and 2 were new compounds,and compounds 3-9 were obtained from the genus Paeonia for the first time.

[Aporphine alkaloids from Litsea greenmaniana].

A new aporphine alkaloid (1), together with five known analogues (2-6), has been isolated from the branch of Litsea greenmaniana by using various chromatographic techniques. Their structures were identified by spectroscopic data analysis ( MS, IR, 1D and 2D NMR) as 2,9-dihydroxy-1,10-dimethoxy-4,5-dihydro-7-oxoaporphine (1), laurotetanine (2), N-methyllaurotetanine (3), isodomesticine (4), isocorydine (5), and norisocorydine (6). Compound 1 was a new compound, and compounds 2-6 were obtained from this plant for the first time.