Algorithms for classification of sequences and segmentation of prostate gland: an external validation study.

OBJECTIVES: The aim of the study was to externally validate two AI models for the classification of prostate mpMRI sequences and segmentation of the prostate gland on T2WI. MATERIALS AND METHODS: MpMRI data from 719 patients were retrospectively collected from two hospitals, utilizing nine MR scanners from four different vendors, over the period from February 2018 to May 2022. Med3D deep learning pretrained architecture was used to perform image classification,UNet-3D was used to segment the prostate gland. The images were classified into one of nine image types by the mode. The segmentation model was validated using T2WI images. The accuracy of the segmentation was evaluated by measuring the DSC, VS,AHD.Finally,efficacy of the models was compared for different MR field strengths and sequences. RESULTS: 20,551 image groups were obtained from 719 MR studies. The classification model accuracy is 99%, with a kappa of 0.932. The precision, recall, and F1 values for the nine image types had statistically significant differences, respectively (all P < 0.001). The accuracy for scanners 1.436 T, 1.5 T, and 3.0 T was 87%, 86%, and 98%, respectively (P < 0.001). For segmentation model, the median DSC was 0.942 to 0.955, the median VS was 0.974 to 0.982, and the median AHD was 5.55 to 6.49 mm,respectively.These values also had statistically significant differences for the three different magnetic field strengths (all P < 0.001). CONCLUSION: The AI models for mpMRI image classification and prostate segmentation demonstrated good performance during external validation, which could enhance efficiency in prostate volume measurement and cancer detection with mpMRI. CLINICAL RELEVANCE STATEMENT: These models can greatly improve the work efficiency in cancer detection, measurement of prostate volume and guided biopsies.

Deep learning-based radiomics model from pretreatment ADC to predict biochemical recurrence in advanced prostate cancer.

Purpose: To develop deep-learning radiomics model for predicting biochemical recurrence (BCR) of advanced prostate cancer (PCa) based on pretreatment apparent diffusion coefficient (ADC) maps. Methods: Data were collected retrospectively from 131 patients diagnosed with advanced PCa, randomly divided into training (n = 93) and test (n = 38) datasets. Pre-treatment ADC images were segmented using a pre-trained artificial intelligence (AI) model to identify suspicious PCa areas. Three models were constructed, including a clinical model, a conventional radiomics model and a deep-radiomics model. The receiver operating characteristic (ROC), precision-recall (PR) curve and decision curve analysis (DCA) were used to assess predictive performance in test dataset. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were employed to compare the performance enhancement of the deep-radiomics model in relation to the other two models. Results: The deep-radiomics model exhibited a significantly higher area under the curve (AUC) of ROC than the other two (P = 0.033, 0.026), as well as PR curve (AUC difference 0.420, 0.432). The DCA curve demonstrated superior performance for the deep-radiomics model across all risk thresholds than the other two. Taking the clinical model as reference, the NRI and IDI was 0.508 and 0.679 for the deep-radiomics model with significant difference. Compared with the conventional radiomics model, the NRI and IDI was 0.149 and 0.164 for the deep-radiomics model without significant difference. Conclusion: The deep-radiomics model exhibits promising potential in predicting BCR in advanced PCa, compared to both the clinical model and the conventional radiomics model.

Quantitative measurement of the ureter on three-dimensional magnetic resonance urography images using deep learning.

BACKGROUND: Accurate measurement of ureteral diameters plays a pivotal role in diagnosing and monitoring urinary tract obstruction (UTO). While three-dimensional magnetic resonance urography (3D MRU) represents a significant advancement in imaging, the traditional manual methods for assessing ureteral diameters are characterized by labor-intensive procedures and inherent variability. In the realm of medical image analysis, deep learning has led to a paradigm shift, yet the development of a comprehensive automated tool for the precise segmentation and measurement of ureters in MR images is an unaddressed challenge. PURPOSE: The ureter was quantitatively measured on 3D MRU images using a deep learning model. METHODS: A retrospective cohort of 445 3D MRU scans (443 patients, 52 ± 18 years; 217 female patients) was collected and split into training, validation, and internal testing cohorts. A 3D V-Net model was trained for urinary tract segmentation, and a post-processing algorithm was developed for ureteral measurements. The accuracy of the segmentation was evaluated using the Dice similarity coefficient (DSC) and volume intraclass correlation coefficient (ICC), with ground truth segmentations provided by experienced radiologists. The external cohort comprised 50 scans (50 patients, 55 ± 21 years; 30 female patients), and the model-predicted ureteral diameter measurements were compared with manual measurements to assess system performance. The various diameter parameters of ureter among the different measurement methods (ground truth, auto-segmentation with automatic diameter extraction, and manual segmentation with automatic diameter extraction) were assessed with Friedman tests and post hoc Dunn test. The effectiveness of the UTO diagnosis was assessed by receiver operating characteristic (ROC) curves and their respective areas under the curve (AUC) between different methods. RESULTS: In both the internal test and external cohorts, the mean DSC values for bilateral ureters exceeded 0.70. The ICCs for the bilateral ureter volume obtained by comparing the model and manual segmentation were all greater than 0.96 (p  < â€¯0.05), except for the right ureter in the internal test cohort, for which the ICC was 0.773 (p  < â€¯0.05). The mean DSCs for interobserver and intraobserver reliability were all above 0.97. The maximum diameter of the ureter exhibited no statistically significant differences either in the dilated (p = 0.08) or in the non-dilated (p = 0.32) ureters across the three measurement methods. The AUCs of ground truth, auto-segmentation with automatic diameter extraction, and manual segmentation with automatic diameter extraction in diagnosing UTO were 0.988 (95% CI: 0.934, 1.000), 0.961 (95% CI: 0.893, 0.991), and 0.979 (95% CI: 0.919, 0.998), respectively. There was no statistical difference between AUCs of the different methods (p > 0.05). CONCLUSION: The proposed deep learning model and post-processing algorithm provide an effective means for the quantitative evaluation of urinary diseases using 3D MRU images.

A quantitative evaluation of the deep learning model of segmentation and measurement of cervical spine MRI in healthy adults.

PURPOSE: To evaluate the 3D U-Net model for automatic segmentation and measurement of cervical spine structures using magnetic resonance (MR) images of healthy adults. MATERIALS AND METHODS: MR images of the cervical spine from 160 healthy adults were collected retrospectively. A previously constructed deep-learning model was used to automatically segment anatomical structures. Segmentation and localization results were checked by experienced radiologists. Pearson's correlation analyses were conducted to examine relationships between patient and image parameters. RESULTS: No measurement was significantly correlated with age or sex. The mean values of the areas of the subarachnoid space and spinal cord from the C2/3 (cervical spine 2-3) to C6/7 intervertebral disc levels were 102.85-358.12 mm2 and 53.71-110.32 mm2 , respectively. The ratios of the areas of the spinal cord to the subarachnoid space were 0.25-0.68. The transverse and anterior-posterior diameters of the subarachnoid space were 14.77-26.56 mm and 7.38-17.58 mm, respectively. The transverse and anterior-posterior diameters of the spinal cord were 9.11-16.02 mm and 5.47-10.12 mm, respectively. CONCLUSION: A deep learning model based on 3D U-Net automatically segmented and performed measurements on cervical spine MR images from healthy adults, paving the way for quantitative diagnosis models for spinal cord diseases.

Combination of clinical information and radiomics models for the differentiation of acute simple appendicitis and non simple appendicitis on CT images.

To investigate the radiomics models for the differentiation of simple and non-simple acute appendicitis. This study retrospectively included 334 appendectomy cases (76 simple and 258 non-simple cases) for acute appendicitis. These cases were divided into training (n = 106) and test cohorts (n = 228). A radiomics model was developed using the radiomic features of the appendix area on CT images as the input variables. A CT model was developed using the clinical and CT features as the input variables. A combined model was developed by combining the radiomics model and clinical information. These models were tested, and their performance was evaluated by receiver operating characteristic curves and decision curve analysis (DCA). The variables independently associated with non-simple appendicitis in the combined model were body temperature, age, percentage of neutrophils and Rad-score. The AUC of the combined model was significantly higher than that of the CT model (P = 0.041). The AUC of the radiomics model was also higher than that of the CT model but did not reach a level of statistical significance (P = 0.053). DCA showed that all three models had a higher net benefit (NB) than the default strategies, and the combined model presented the highest NB. A nomogram of the combined model was developed as the graphical representation of the final model. It is feasible to use the combined information of clinical and CT radiomics models for the differentiation of simple and non-simple acute appendicitis.

Development and validation of functional kompetitive allele-specific PCR markers for herbicide resistance in Brassica napus.

Effective weed control in the field is essential for maintaining favorable growing conditions and rapeseed yields. Sulfonylurea herbicides are one kind of most widely used herbicides worldwide, which control weeds by inhibiting acetolactate synthase (ALS). Molecular markers have been designed from polymorphic sites within the sequences of ALS genes, aiding marker-assisted selection in breeding herbicide-resistant rapeseed cultivars. However, most of them are not breeder friendly and have relatively limited application due to higher costs and lower throughput in the breeding projects. The aims of this study were to develop high throughput kompetitive allele-specific PCR (KASP) assays for herbicide resistance. We first cloned and sequenced BnALS1 and BnALS3 genes from susceptible cultivars and resistant 5N (als1als1/als3als3 double mutant). Sequence alignments of BnALS1 and BnALS3 genes for cultivars and 5N showed single nucleotide polymorphisms (SNPs) at positions 1676 and 1667 respectively. These two SNPs for BnALS1 and BnALS3 resulted in amino acid substitutions and were used to develop a KASP assay. These functional markers were validated in three distinct BC1F2 populations. The KASP assay developed in this study will be valuable for the high-throughput selection of elite materials with high herbicide resistance in rapeseed breeding programs.

Advances in Doxorubicin-based nano-drug delivery system in triple negative breast cancer.

Triple positive breast cancer (TPBC) is one of the most aggressive breast cancer. Due to the unique cell phenotype, aggressiveness, metastatic potential and lack of receptors or targets, chemotherapy is the choice of treatment for TNBC. Doxorubicin (DOX), one of the representative agents of anthracycline chemotherapy, has better efficacy in patients with metastatic TNBC (mTNBC). DOX in anthracycline-based chemotherapy regimens have higher response rates. Nano-drug delivery systems possess unique targeting and ability of co-load, deliver and release chemotherapeutic drugs, active gene fragments and immune enhancing factors to effectively inhibit or kill tumor cells. Therefore, advances in nano-drug delivery systems for DOX therapy have attracted a considerable amount of attention from researchers. In this article, we have reviewed the progress of nano-drug delivery systems (e.g., Nanoparticles, Liposomes, Micelles, Nanogels, Dendrimers, Exosomes, etc.) applied to DOX in the treatment of TNBC. We also summarize the current progress of clinical trials of DOX combined with immune checkpoint inhibitors (ICIS) for the treatment of TNBC. The merits, demerits and future development of nanomedicine delivery systems in the treatment of TNBC are also envisioned, with the aim of providing a new class of safe and efficient thoughts for the treatment of TNBC.

Multi-omics strategies uncover the molecular mechanisms of nitrogen, phosphorus and potassium deficiency responses in Brassica napus.

BACKGROUND: Nitrogen (N), phosphorus (P) and potassium (K) are critical macronutrients in crops, such that deficiency in any of N, P or K has substantial effects on crop growth. However, the specific commonalities of plant responses to different macronutrient deficiencies remain largely unknown. METHODS: Here, we assessed the phenotypic and physiological performances along with whole transcriptome and metabolomic profiles of rapeseed seedlings exposed to N, P and K deficiency stresses. RESULTS: Quantities of reactive oxygen species were significantly increased by all macronutrient deficiencies. N and K deficiencies resulted in more severe root development responses than P deficiency, as well as greater chlorophyll content reduction in leaves (associated with disrupted chloroplast structure). Transcriptome and metabolome analyses validated the macronutrient-specific responses, with more pronounced effects of N and P deficiencies on mRNAs, microRNAs (miRNAs), circular RNAs (circRNAs) and metabolites relative to K deficiency. Tissue-specific responses also occurred, with greater effects of macronutrient deficiencies on roots compared with shoots. We further uncovered a set of common responders with simultaneous roles in all three macronutrient deficiencies, including 112 mRNAs and 10 miRNAs involved in hormonal signaling, ion transport and oxidative stress in the root, and 33 mRNAs and 6 miRNAs with roles in abiotic stress response and photosynthesis in the shoot. 27 and seven common miRNA-mRNA pairs with role in miRNA-mediated regulation of oxidoreduction processes and ion transmembrane transport were identified in all three macronutrient deficiencies. No circRNA was responsive to three macronutrient deficiency stresses, but two common circRNAs were identified for two macronutrient deficiencies. Combined analysis of circRNAs, miRNAs and mRNAs suggested that two circRNAs act as decoys for miR156 and participate in oxidoreduction processes and transmembrane transport in both N- and P-deprived roots. Simultaneously, dramatic alterations of metabolites also occurred. Associations of RNAs with metabolites were observed, and suggested potential positive regulatory roles for tricarboxylic acids, azoles, carbohydrates, sterols and auxins, and negative regulatory roles for aromatic and aspartate amino acids, glucosamine-containing compounds, cinnamic acid, and nicotianamine in plant adaptation to macronutrient deficiency. CONCLUSIONS: Our findings revealed strategies to rescue rapeseed from macronutrient deficiency stress, including reducing the expression of non-essential genes and activating or enhancing the expression of anti-stress genes, aided by plant hormones, ion transporters and stress responders. The common responders to different macronutrient deficiencies identified could be targeted to enhance nutrient use efficiency in rapeseed.

Automatic renal mass segmentation and classification on CT images based on 3D U-Net and ResNet algorithms.

Purpose: To automatically evaluate renal masses in CT images by using a cascade 3D U-Net- and ResNet-based method to accurately segment and classify focal renal lesions. Material and Methods: We used an institutional dataset comprising 610 CT image series from 490 patients from August 2009 to August 2021 to train and evaluate the proposed method. We first determined the boundaries of the kidneys on the CT images utilizing a 3D U-Net-based method to be used as a region of interest to search for renal mass. An ensemble learning model based on 3D U-Net was then used to detect and segment the masses, followed by a ResNet algorithm for classification. Our algorithm was evaluated with an external validation dataset and kidney tumor segmentation (KiTS21) challenge dataset. Results: The algorithm achieved a Dice similarity coefficient (DSC) of 0.99 for bilateral kidney boundary segmentation in the test set. The average DSC for renal mass delineation using the 3D U-Net was 0.75 and 0.83. Our method detected renal masses with recalls of 84.54% and 75.90%. The classification accuracy in the test set was 86.05% for masses (<5 mm) and 91.97% for masses (≥5 mm). Conclusion: We developed a deep learning-based method for fully automated segmentation and classification of renal masses in CT images. Testing of this algorithm showed that it has the capability of accurately localizing and classifying renal masses.

Age-related morphometrics of normal adrenal glands based on deep learning-aided segmentation.

OBJECTIVE: This study aims to evaluate the morphometrics of normal adrenal glands in adult patients semiautomatically using a deep learning-based segmentation model. MATERIALS AND METHODS: A total of 520 abdominal CT image series with normal findings, from January 1, 2016, to March 14, 2019, were retrospectively collected for the training of the adrenal segmentation model. Then, 1043 portal venous phase image series of inpatient contrast-enhanced abdominal CT examinations with normal adrenal glands were included for analysis and grouped by every 10-year gap. A 3D U-Net-based segmentation model was used to predict bilateral adrenal labels followed by manual modification of labels as appropriate. Quantitative parameters (volume, CT value, and diameters) of the bilateral adrenal glands were then analyzed. RESULTS: In the study cohort aged 18-77 years old (554 males and 489 females), the left adrenal gland was significantly larger than the right adrenal gland [all patients, 2867.79 (2317.11-3499.89) mm3 vs. 2452.84 (1983.50-2935.18) mm3, P < 0.001]. Male patients showed a greater volume of bilateral adrenal glands than females in all age groups (all patients, left: 3237.83 ± 930.21 mm3 vs. 2646.49 ± 766.42 mm3, P < 0.001; right: 2731.69 ± 789.19 mm3 vs. 2266.18 ± 632.97 mm3, P = 0.001). Bilateral adrenal volume in male patients showed an increasing then decreasing trend as age increased that peaked at 38-47 years old (left: 3416.01 ± 886.21 mm3, right: 2855.04 ± 774.57 mm3). CONCLUSIONS: The semiautomated measurement revealed that the adrenal volume differs as age increases. Male patients aged 38-47 years old have a peaked adrenal volume.

Deep learning for fully automated segmentation and volumetry of Couinaud liver segments and future liver remnants shown with CT before major hepatectomy: a validation study of a predictive model.

Background: Recent reports have shown the potential for deep learning (DL) models to automatically segment of Couinaud liver segments and future liver remnant (FLR) for liver resections. However, these studies have mainly focused on the development of the models. Existing reports lack adequate validation of these models in diverse liver conditions and thorough evaluation using clinical cases. This study thus aimed to develop and perform a spatial external validation of a DL model for the automated segmentation of Couinaud liver segments and FLR using computed tomography (CT) in various liver conditions and to apply the model prior to major hepatectomy. Methods: This retrospective study developed a 3-dimensional (3D) U-Net model for the automated segmentation of Couinaud liver segments and FLR on contrast-enhanced portovenous phase (PVP) CT scans. Images were obtained from 170 patients from January 2018 to March 2019. First, radiologists annotated the Couinaud segmentations. Then, a 3D U-Net model was trained in Peking University First Hospital (n=170) and tested in Peking University Shenzhen Hospital (n=178) in cases with various liver conditions (n=146) and in candidates for major hepatectomy (n=32). The segmentation accuracy was evaluated using the dice similarity coefficient (DSC). Quantitative volumetry to evaluate the resectability was compared between manual and automated segmentation. Results: The DSC in the test data sets 1 and 2 for segments I to VIII was 0.93±0.01, 0.94±0.01, 0.93±0.01, 0.93±0.01, 0.94±0.00, 0.95±0.00, 0.95±0.00, and 0.95±0.00, respectively. The mean automated FLR and FLR% assessments were 493.51±284.77 mL and 38.53%±19.38%, respectively. The mean manual FLR and FLR% assessments were 500.92±284.38 mL and 38.35%±19.14%, respectively, in test data sets 1 and 2. For test data set 1, when automated segmentation of the FLR% was used, 106, 23, 146, and 57 cases were categorized as candidates for a virtual major hepatectomy of types 1, 2, 3, and 4, respectively; however, when manual segmentation of the FLR% was used, 107, 23, 146, and 57 cases were categorized as candidates for a virtual major hepatectomy of types 1, 2, 3, and 4, respectively. For test data set 2, all cases were categorized as candidates for major hepatectomy when automated and manual segmentation of the FLR% was used. No significant differences in FLR assessment (P=0.50; U=185,545), FLR% assessment (P=0.82; U=188,337), or the indications for major hepatectomy were noted between automated and manual segmentation (McNemar test statistic 0.00; P>0.99). Conclusions: The DL model could be used to fully automate the segmentation of Couinaud liver segments and FLR with CT prior to major hepatectomy in an accurate and clinically practicable manner.

Semiautomated pelvic lymph node treatment response evaluation for patients with advanced prostate cancer: based on MET-RADS-P guidelines.

BACKGROUND: The evaluation of treatment response according to METastasis Reporting and Data System for Prostate Cancer (MET-RADS-P) criteria is an important but time-consuming task for patients with advanced prostate cancer (APC). A deep learning-based algorithm has the potential to assist with this assessment. OBJECTIVE: To develop and evaluate a deep learning-based algorithm for semiautomated treatment response assessment of pelvic lymph nodes. METHODS: A total of 162 patients who had undergone at least two scans for follow-up assessment after APC metastasis treatment were enrolled. A previously reported deep learning model was used to perform automated segmentation of pelvic lymph nodes. The performance of the deep learning algorithm was evaluated using the Dice similarity coefficient (DSC) and volumetric similarity (VS). The consistency of the short diameter measurement with the radiologist was evaluated using Bland-Altman plotting. Based on the segmentation of lymph nodes, the treatment response was assessed automatically with a rule-based program according to the MET-RADS-P criteria. Kappa statistics were used to assess the accuracy and consistency of the treatment response assessment by the deep learning model and two radiologists [attending radiologist (R1) and fellow radiologist (R2)]. RESULTS: The mean DSC and VS of the pelvic lymph node segmentation were 0.82 ± 0.09 and 0.88 ± 0.12, respectively. Bland-Altman plotting showed that most of the lymph node measurements were within the upper and lower limits of agreement (LOA). The accuracies of automated segmentation-based assessment were 0.92 (95% CI: 0.85-0.96), 0.91 (95% CI: 0.86-0.95) and 75% (95% CI: 0.46-0.92) for target lesions, nontarget lesions and nonpathological lesions, respectively. The consistency of treatment response assessment based on automated segmentation and manual segmentation was excellent for target lesions [K value: 0.92 (0.86-0.98)], good for nontarget lesions [0.82 (0.74-0.90)] and moderate for nonpathological lesions [0.71 (0.50-0.92)]. CONCLUSION: The deep learning-based semiautomated algorithm showed high accuracy for the treatment response assessment of pelvic lymph nodes and demonstrated comparable performance with radiologists.

Integrating radiomics with the vesical imaging-reporting and data system to predict muscle invasion of bladder cancer.

PURPOSE: To develop predictive models based on the integration of radiomics with the Vesical Imaging-Reporting and Data System (VI-RADS) for determining muscle invasion of bladder cancer. MATERIALS AND METHODS: One hundred ninety-one patients were retrospectively included in this study from January 2015 to March 2022. Of these, 121 data were randomly divided into training and validation sets at a ratio of 7:3. The remaining data (n = 70) served as the independent testing set. The radiomics features were extracted from bladder cancer on high-b-value DWI images. The pipelines of radiomics models were trained in the training set. One optimal model was selected based on the performance in the validation set. Then, the selected model was tested in the independent testing set. Two radiologists evaluated the VI-RADS based on T2WI and DWI. Reader 1 was an experienced reader, and Reader 2 was an inexperienced reader. A clinical-radiomics model was built by integrating the radiomics signature and VI-RADS. The performance was assessed using receiver operating characteristic curve analysis. The histopathological results were used as the standard reference to assess the diagnostic accuracy of muscle invasion. RESULTS: The radiomics model had area under the curve (AUC) values of 0.801, 0.867, and 0.806 in the training, validation, and testing sets, respectively. The VI-RADS scores of Readers 1/2 yielded AUC values of 0.831/0.781, 0.909/0.815, and 0.871/0.776 in the training, validation, and testing sets, respectively. The clinical-radiomics model for Readers 1/2 revealed AUC values of 0.889/0.854, 0.961/0.919, and 0.881/0.844 in the training, validation, and testing sets, respectively. The performance of the clinical-radiomics model was improved compared to the VI-RADS score for inexperienced Reader 2 (P < 0.05). CONCLUSION: The radiomics model was useful in the diagnosis of muscle invasion of bladder cancer. The clinical-radiomics model integrating radiomics and VI-RADS further improved the performance compared to VI-RADS alone, which was helpful for readers with less diagnostic experience.

A preliminary radiomics model for predicting perirenal fat invasion on renal cell carcinoma with contrast-enhanced CT images.

OBJECTIVE: The aim is to develop a radiomics model based on contrast-enhanced CT scans for preoperative prediction of perirenal fat invasion (PFI) in patients with renal cell carcinoma (RCC). METHODS: The CT data of 131 patients with pathology-confirmed PFI status (64 positives) were retrospectively collected and randomly assigned to the training and test datasets. The kidneys and the masses were annotated by semi-automatic segmentation. Eight types of regions of interest (ROI) were chosen for the training of the radiomics models. The areas under the curves (AUCs) from the receiver operating characteristic (ROC) curve analysis were used to analyze the diagnostic performance. Eight types of models with different ROIs have been developed. The models with the highest AUC in the test dataset were used for construction of the corresponding final model, and comparison with radiologists' diagnosis. RESULTS: The AUCs of the models for each ROI was 0.783-0.926, and there was no statistically significant difference between them (P > 0.05). Model 4 was using the ROI of the outer half-ring which extended along the edge of the mass at the outer edge of the kidney into the perirenal fat space with a thickness of 3 mm. It yielded the highest AUC (0.926) and its diagnostic accuracy was higher than the radiologists' diagnosis. CONCLUSION: We have developed and validated a radiomics model for prediction of PFI on RCC with contrast-enhanced CT scans. The model proved to be more accurate than the radiologists' diagnosis.

Automatic measurement of the patellofemoral joint parameters in the Laurin view: a deep learning-based approach.

OBJECTIVES: To explore the performance of a deep learning-based algorithm for automatic patellofemoral joint (PFJ) parameter measurements from the Laurin view. METHODS: A total of 1431 consecutive Laurin views of the PFJ were retrospectively collected and divided into two parts: (1) the model development dataset (dataset 1, n = 1230) and (2) the hold-out test set (dataset 2, n = 201). Dataset 1 was used to develop the U-shaped fully convolutional network (U-Net) model to segment the landmarks of the PFJ. Based on the predicted landmarks, the PFJ parameters were calculated, including the sulcus angle (SA), congruence angle (CA), patellofemoral ratio (PFR), and lateral patellar tilt (LPT). Dataset 2 was used to assess the model performance. The mean of three radiologists who independently measured the PFJ parameters was defined as the reference standard. Model performance was assessed by the intraclass correlation coefficient (ICC), mean absolute difference (MAD), and root mean square (RMS) compared to the reference standard. Ninety-five percent limits of agreement (95% LoA) were calculated pairwise for each radiologist, reference standard, and model. RESULTS: Compared with the reference standard, U-Net showed good performance for predicting SA, CA, PFR, and LPT, with ICC = 0.85-0.97, MAD = 0.06-5.09, and RMS = 0.09-6.90 in the hold-out test set. Except for the PFR, the remaining parameters measured between the reference standard and the model were within the 95% LoA in the hold-out test dataset. CONCLUSIONS: The U-Net-based deep learning approach had a relatively high model performance in automatically measuring SA, CA, PFR, and LPT. KEY POINTS: • The U-Net model could be used to segment the landmarks of the PFJ and calculate the SA, CA, PFR, and LPT, which could be used to evaluate the patellar instability. • In the hold-out test, the automatic measurement model yielded comparable performance with reference standard. • The automatic measurement model could still accurately predict SA, CA, PFR, and LPT in patients with PI and/or PFOA.

Automatic diagnosis and grading of patellofemoral osteoarthritis from the axial radiographic view: a deep learning-based approach.

BACKGROUND: Patellofemoral osteoarthritis (PFOA) has a high prevalence and is assessed on axial radiography of the patellofemoral joint (PFJ). A deep learning (DL)-based approach could help radiologists automatically diagnose and grade PFOA via interpreting axial radiographs. PURPOSE: To develop and assess the performance of a DL-based approach for diagnosing and grading PFOA on axial radiographs. MATERIAL AND METHODS: A total of 1280 (dataset 1) axial radiographs were retrospectively collected and utilized to develop the high-resolution network (HRNet)-based classification models. The ground truth was the interpretation from two experienced radiologists in consensus according to the K-L grading system. A binary-class model was trained to diagnose the presence (K-L 2∼4) or absence (K-L 0∼1) of PFOA. A multi-class model was used to grade the stage of PFOA, i.e. from K-L 0 to K-L 4. Model performances were evaluated using the receiver operating characteristics (ROC), confusion matrix, and the corresponding evaluation metrics (positive predictive value [PPV], negative predictive value [NPV], F1 score, sensitivity, specificity, accuracy) of the internal test set (n = 129) from dataset 1 and an external validation set (dataset 2, n = 187). RESULTS: For the binary-class model, the area under the curve (AUC) was 0.91 in the internal test set and 0.90 in the external validation set. For grading PFOA, moderate to severe stage of PFOA exhibited a good performance in these two datasets (AUC = 0.91-0.98, PPV = 0.69-0.90, NPV = 0.92-0.99, F1 score = 0.72-0.87, sensitivity = 0.75-0.87, specificity = 0.90-0.99, accuracy = 0.87-0.98). CONCLUSION: The HRNet-based approach performed well in diagnosing and grading radiographic PFOA, especially for the moderate to severe cases.

Automatic segmentation of hepatic metastases on DWI images based on a deep learning method: assessment of tumor treatment response according to the RECIST 1.1 criteria.

BACKGROUND: Evaluation of treated tumors according to Response Evaluation Criteria in Solid Tumors (RECIST) criteria is an important but time-consuming task in medical imaging. Deep learning methods are expected to automate the evaluation process and improve the efficiency of imaging interpretation. OBJECTIVE: To develop an automated algorithm for segmentation of liver metastases based on a deep learning method and assess its efficacy for treatment response assessment according to the RECIST 1.1 criteria. METHODS: One hundred and sixteen treated patients with clinically confirmed liver metastases were enrolled. All patients had baseline and post-treatment MR images. They were divided into an initial (n = 86) and validation cohort (n = 30) according to the examined time. The metastatic foci on DWI images were annotated by two researchers in consensus. Then the treatment responses were assessed by the two researchers according to RECIST 1.1 criteria. A 3D U-Net algorithm was trained for automated liver metastases segmentation using the initial cohort. Based on the segmentation of liver metastases, the treatment response was assessed automatically with a rule-based program according to the RECIST 1.1 criteria. The segmentation performance was evaluated using the Dice similarity coefficient (DSC), volumetric similarity (VS), and Hausdorff distance (HD). The area under the curve (AUC) and Kappa statistics were used to assess the accuracy and consistency of the treatment response assessment by the deep learning model and compared with two radiologists [attending radiologist (R1) and fellow radiologist (R2)] in the validation cohort. RESULTS: In the validation cohort, the mean DSC, VS, and HD were 0.85 ± 0.08, 0.89 ± 0.09, and 25.53 ± 12.11 mm for the liver metastases segmentation. The accuracies of R1, R2 and automated segmentation-based assessment were 0.77, 0.65, and 0.74, respectively, and the AUC values were 0.81, 0.73, and 0.83, respectively. The consistency of treatment response assessment based on automated segmentation and manual annotation was moderate [K value: 0.60 (0.34-0.84)]. CONCLUSION: The deep learning-based liver metastases segmentation was capable of evaluating treatment response according to RECIST 1.1 criteria, with comparable results to the junior radiologist and superior to that of the fellow radiologist.

Utility of diffusion weighted imaging-based radiomics nomogram to predict pelvic lymph nodes metastasis in prostate cancer.

BACKGROUND: Preoperative pelvic lymph node metastasis (PLNM) prediction can help clinicians determine whether to perform pelvic lymph node dissection (PLND). The purpose of this research is to explore the feasibility of diffusion-weighted imaging (DWI)-based radiomics for preoperative PLNM prediction in PCa patients at the nodal level. METHODS: The preoperative MR images of 1116 pathologically confirmed lymph nodes (LNs) from 84 PCa patients were enrolled. The subjects were divided into a primary cohort (67 patients with 192 positive and 716 negative LNs) and a held-out cohort (17 patients with 43 positive and 165 negative LNs) at a 4:1 ratio. Two preoperative pelvic lymph node metastasis (PLNM) prediction models were constructed based on automatic LN segmentation with quantitative radiological LN features alone (Model 1) and combining radiological and radiomics features (Model 2) via multiple logistic regression. The visual assessments of junior (Model 3) and senior (Model 4) radiologists were compared. RESULTS: No significant difference was found between the area under the curve (AUCs) of Models 1 and 2 (0.89 vs. 0.90; P = 0.573) in the held-out cohort. Model 2 showed the highest AUC (0.83, 95% CI 0.76, 0.89) for PLNM prediction in the LN subgroup with a short diameter ≤ 10 mm compared with Model 1 (0.78, 95% CI 0.70, 0.84), Model 3 (0.66, 95% CI 0.52, 0.77), and Model 4 (0.74, 95% CI 0.66, 0.88). The nomograms of Models 1 and 2 yielded C-index values of 0.804 and 0.910, respectively, in the held-out cohort. The C-index of the nomogram analysis (0.91) and decision curve analysis (DCA) curves confirmed the clinical usefulness and benefit of Model 2. CONCLUSIONS: A DWI-based radiomics nomogram incorporating the LN radiomics signature with quantitative radiological features is promising for PLNM prediction in PCa patients, particularly for normal-sized LNM.

CRISPR/Cas9-Mediated Gene Editing of BnFAD2 and BnFAE1 Modifies Fatty Acid Profiles in Brassica napus.

Fatty acid (FA) composition determines the quality of oil from oilseed crops, and thus is a major target for genetic improvement. FAD2 (Fatty acid dehydrogenase 2) and FAE1 (fatty acid elongase 1) are critical FA synthetic genes, and have been the focus of genetic manipulation to alter fatty acid composition in oilseed plants. In this study, to improve the nutritional quality of rapeseed cultivar CY2 (about 50% oil content; of which 40% erucic acid), we generated novel knockout plants by CRISPR/Cas9 mediated genome editing of BnFAD2 and BnFAE1 genes. Two guide RNAs were designed to target one copy of the BnFAD2 gene and two copies of the BnFAE1 gene, respectively. A number of lines with mutations at three target sites of BnFAD2 and BnFAE1 genes were identified by sequence analysis. Three of these lines showed mutations in all three target sites of the BnFAD2 and BnFAE1 genes. Fatty acid composition analysis of seeds revealed that mutations at all three sites resulted in significantly increased oleic acid (70-80%) content compared with that of CY2 (20%), greatly reduced erucic acid levels and slightly decreased polyunsaturated fatty acids content. Our results confirmed that the CRISPR/Cas9 system is an effective tool for improving this important trait.

Prediction of pelvic lymph node metastasis in prostate cancer using radiomics based on T2-weighted imaging. / 基于T2åŠ æƒæˆåƒçš„å½±åƒç»„å­¦åˆ—çº¿å›¾æ¨¡åž‹é¢„æµ‹å‰åˆ—è ºç™Œç›†è ”æ·‹å·´ç»“è½¬ç§».

OBJECTIVES: Pelvic lymph node metastasis (PLNM) is an important factor that affects the stage and prognosis of prostate cancer. Invasive extended pelvic lymph node dissection (ePLND) is the most effective method for clinically diagnosing PLNM. Accurate preoperative prediction of PLNM can reduce unnecessary ePLND. This study aims to investigate the clinical value of radiomics nomogram in predicting PLNM of prostate cancer based on T2-weighted imaging (T2WI). METHODS: Magnetic resonance (MR) data of 71 patients with prostate cancer who underwent ePLND from January 2017 to June 2021 in Peking University First Hospital were collected retrospectively. All patients were assigned into a training set (January 2017 to December 2020, n=56, containing 186 lymph nodes) and a test set (January 2021 to June 2021, n=15, containing 45 lymph nodes) according to the examination time of multiparametric magnetic resonance imaging (mpMRI). Two radiologists matched the dissected lymph nodes on MRI images, and manually annotated the region of interest (ROI). Based on the outlined ROI, 3 metastatic lymph node prediction models were established: Model 1 (only image features of T2WI), Model 2 (radiomics features based on random forest), and Model 3 (combination of the image and radiomics features). A nomogram was also established. The clinicopathologic characteristics of the patients were obtained from the medical records, including age, the Gleason score, the level of prostate-specific antigen (PSA), and clinical and pathological T stage. The preoperative radiological features of the pelvic lymph nodes (LNs) include size of LNs (the short and long diameters) and volume of LNs. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the 3 models and decision curve analysis (DCA) was used to evaluate the clinical benefits of the models. RESULTS: No significant differences were found between the training set and test set regarding age, Gleason scores, PSA level, and clinical and pathological T stage (all P>0.05). The differences in volume, short diameter and long diameter between metastatic and non-metastatic LNs were statistically significant in both training set and test set (all P<0.05). In multivariate regression analysis, the short diameter and marginal status of LNs were included in Model 1. Eighteen omics features were selected to construct Model 2. The signal distribution of LNs and Rad score were the significant risk factors for predicting metastasis of pelvic LNs in Model 3. The C-index of nomogram based on Model 3 reached 0.964, and the calibration curve showed that the model had high calibration degree. In the test set, the area under the curves of Model 1, 2, and 3 were 0.78, 0.93, and 0.96 respectively, Model 2 and Model 3 showed significantly higher diagnostic efficiency than Model 1 (Model 1 vs Model 2, P=0.019; Model 1 vs Model 3, P=0.020). There was no significant difference in the area under the curve between Model 2 and Model 3 (P=0.649). The DCA results of the 3 models showed that all models obtained higher net benefits than the PLNM-all or PLNM-none protocol in different ranges of threshold probabilities and Model 3 had the highest clinical benefit. CONCLUSIONS: The radiomics nomogram based on T2WI shows a good predictive efficacy for preoperative PLNM in patients with prostate cancer, which could be served as an imaging biomarker to optimize decision-making and adjust adjuvant treatments.