Objective:To investigate the predictive value of conventional ultrasound，intratumoral and peritumoral radiomics models based on ultrasound images for the efficacy of neoadjuvant therapy in triple negative breast cancer（TNBC），and to construct a combined model.Methods:A total of 122 patients diagnosed with TNBC and admitted to Nanjing Drum Tower Hospital，Affiliated Hospital of Medical School，Nanjing University between April 2020 and December 2023 were retrospectively selected as study subjects. All patients underwent surgical resection after neoadjuvant therapy and were categorized into two groups：50 cases in the pathological complete remission（pCR）group and 72 cases in the non-pCR group according to surgical pathology. A comprehensive array of clinical data，along with conventional ultrasound imaging findings，was systematically collected from patients prior to treatment and at the conclusion of the second cycle of treatment. The region of interest（ROI）was delineated on the baseline two-dimensional gray-scale ultrasound image using 3D Slicer software on the maximum long-axis section of the lesion. The peri-tumor area was obtained by circularly expanding outward by 5 mm，and the imaging histological features were extracted separately. The dataset was then randomly partitioned into a training set and a validation set，with a ratio of 7∶3. The χ2/ t/Mann-Whitney U tests were used for intergroup comparison of general information. Maximum correlation minimum redundancy and least absolute shrinkage and selection operator regression were used to screen the optimal radiomics feature set，respectively. Variables that exhibited statistically significant differences between group comparisons were then employed to construct combined models，incorporating intratumor and peritumor ultrasonographic models. The predictive efficacy，accuracy，and clinical utility of the models were assessed using ROC curves，calibration curves，and decision curve analysis（DCA），respectively. Results:Subsequent between-group comparisons and multifactorial Logistic regression analysis identified blood flow（ OR=0.213，95% CI=0.062-0.735）and change rate of length diameter（ OR=1.091，95% CI=1.013-1.175）as independent risk factors for predicting pCR. A total of seven and eight radiomics features from each of the intratumoral and peritumoral regions were screened for the construction of intratumoral imaging histology score（RS）and peritumoral RS. The Nomogram model was constructed by combining the blood flow，change rate of length diameter，intratumoral RS，and peritumoral RS，and its AUC values in the training and validation sets were 0.884（95% CI=0.815-0.953）and 0.841（95% CI=0.683-0.940），respectively. The calibration curves demonstrated the Nomogram model's exceptional precision，with a C-index of 0.860 and 0.782 for the training and validation sets，respectively. The DCA revealed that the Nomogram model exhibited the optimal net clinical benefit. Conclusions:Conventional ultrasound，intratumoral and peritumoral radiomics models based on ultrasound images have been shown to possess satisfactory predictive value for the efficacy of neoadjuvant therapy in TNBC，thereby facilitating clinical decision-making.

Objective:To investigate the predictive value of 18F-FDG PET related metabolic parameters on microsatellite instability-high (MSI-H) and human epidermal growth factor receptor-2(HER2) expression in colorectal cancer (CRC). Methods:The 18F-FDG PET imaging, clinical and pathological data of 101 CRC patients (58 males, 43 females; age 68.0(58.0, 75.0) years) admitted to Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University from January 2016 to March 2024 were retrospectively collected, including 17 cases in MSI-H group, 84 cases in microsatellite stability/microsatellite instability-low (MSS/MSI-L) group, 23 cases in HER2 expression group, 34 cases in non-HER2 expression group, and 44 patients without HER2 detection. Univariate analyses (independent-sample t test, Mann-Whitney U test, χ2 test) and multivariate logistic regression analysis were used to screen out independent risk factors, and ROC curve was used to evaluate the predictive efficacy. Bootstrap method was used to verify the model internally. Results:There were significant differences in metabolic tumor volume (MTV) 80% between MSI-H group and MSS/MSI-L group (2.1(1.6, 4.0) vs 1.4(1.0, 2.7) cm 3;Z=-2.10, P=0.036), and total lesion glycolysis (TLG) 80% and carcinoembryonic antigen (CEA) were significantly different between those 2 groups ( Z=-2.27, χ2=6.40, both P<0.05). There were significant differences in TLG 80% (29.0(16.1, 41.0) vs 14.3(9.4, 22.9) g; Z=-2.80, P=0.005) between HER2 expression group and non-HER2 expression group, and significant differences were also found in MTV 80%, heterogeneity index (HI) and CV ( Z=-2.24, t values: -2.26, 2.54, all P<0.05). The independent risk factors for MSI-H were MTV 80% (odds ratio ( OR)=1.326, 95% CI: 1.015-1.733, P=0.038) and CEA ( OR=0.200, 95% CI: 0.056-0.706, P=0.012), with the AUC for the combined model of 0.730 (95% CI: 0.605-0.856), and the concordance index (C-index) of 0.716. The independent risk factors for HER2 expression were TLG 80% ( OR=1.037, 95% CI: 1.001-1.073, P=0.041) and CV ( OR=1.467, 95% CI: 1.073-2.005, P=0.016), with the AUC for the combined model of 0.775 (95% CI: 0.645-0.875), and the C-index of 0.757. Conclusions:18F-FDG PET can be used as a noninvasive tool to evaluate CRC microsatellite status and HER2 gene amplification. MTV 80% and CEA are independent risk factors for MSI-H; TLG 80% and CV are independent risk factors for HER2 expression.

Objective:To evaluate the diagnostic value of multimodal Nomogram model combining 18F-FDG PET/CT and ultrasound for triple negative breast cancer (TNBC) . Methods:A total of 61 breast cancer patients admitted at Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School from November 2016 to May 2024 were selected as the study subjects, including 12 cases of TNBC and 49 cases of non-TNBC. 18F-FDG PET/CT metabolic parameters maximum standardized uptake value (SUV max), mean standardized uptake value (SUV mean), minimum standardized uptake value (SUV min), tumor metabolic volume (MTV), and total lesion glycolysis (TLG), as well as the ultrasound parameters long diameter, short diameter, echogenicity, morphology, boundaries, posterior echogenicity, aspect ratio, microcalcifications, blood flow grading and Breast Imaging Reporting and Data System (BI-RADS) grading were compared between patients with and without TNBC. Least absolute shrinkage and selection operator (LASSO) regression was used for feature screening, and binary multivariate logistic regression analysis was conducted on the screened variables to obtain the independent influencing factors for diagnosing TNBC. The independent factors influencing the diagnosis of TNBC were established as Nomogram model and visualized. Receiver operator characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to evaluate the diagnostic efficacy, accuracy and clinical practicability of the model, respectively. Results:There were statistically significant differences in SUV max ( Z=-2.43, P=0.015), SUV mean ( Z=-2.54, P=0.011), morphology ( P=0.004), boundaries ( χ2=4.86, P=0.028), posterior echogenicity ( P=0.027), and blood flow grading ( χ2=4.52, P=0.034) between TNBC and non-TNBC patients. LASSO regression screened out three variables: SUV max, morphology and blood flow grading. Multivariate analysis showed that, SUV max ( OR=1.20, 95% CI: 1.04-1.38, P=0.012), morphology ( OR=0.02, 95% CI: 0.01-0.49, P=0.016), and blood flow grading ( OR=0.06, 95% CI: 0.01-0.74, P=0.028) were the independent influencing factors for diagnosing TNBC. A Nomogram model was established based on the above independent influencing factors. ROC curve showed that, area under the curve (AUC) of SUV max, morphology, blood flow grading, and the Nomogram model were 0.73 (95% CI: 0.60-0.83), 0.66 (95% CI: 0.52-0.77), 0.67 (95% CI: 0.54-0.79), 0.90 (95% CI: 0.79-0.96), respectively, and the diagnostic value of the Nomogram model was higher than that of SUV max ( Z=2.71, P=0.007), morphology ( Z=3.61, P<0.001), and blood flow grading ( Z=2.51, P=0.012) alone. Calibration curve and DCA showed better accuracy and clinical practicability of the Nomogram model. Conclusions:Nomogram model constructed by combining the SUV max of 18F-FDG PET/CT with the morphology and blood flow grading of ultrasound has a promising potential for diagnosing TNBC.

Objective:To explore the predictive value of ultrasonic shear wave elastography (SWE) parameters and pathological characteristics on the status of human epidermal growth factor receptor 2 (HER2), which is difficult to be determined by immunohistochemistry (IHC) in breast cancer, and to construct a nomogram model.Methods:A retrospective case-control study was conducted. One hundred and fifteen cases of breast cancer diagnosed and treated in Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University from September 2018 to April 2022 were selected, and their HER2 was evaluated as IHC 2+; the HER2 expression status was determined by fluorescence in situ hybridization (FISH) detection, including 23 HER2 positive cases and 92 HER2 negative cases. The ultrasound SWE parameters [including maximum shear wave velocity (V max), mean shear wave velocity (V mean), median shear wave velocity (V median), minimum shear wave velocity (V min)] and clinicopathological characteristics between HER2 positive and negative groups were compared. The variables with statistically significant differences ( P < 0.05) between groups were included in a multivariate logistic regression model, the independent risk factors for HER2 positivity were screened, and a nomogram model was constructed based on these independent risk factors. With the FISH test results as the gold standard, the efficacy of nomogram in judging HER2 positivity in breast cancer which was difficult to be identified by IHC was evaluated with the receiver operating characteristic (ROC) curve; the accuracy and clinical net benefit of the nomogram model were evaluated using calibration curve and decision curve analysis (DCA), respectively. Results:The patients were all female, aged (56±13) years, ranging from 30 to 88 years old. V max [ M ( Q1, Q3)] [8.54 (7.38, 9.47) m/s vs. 6.46 (5.07, 8.42) m/s], V mean [(5.41±0.78) m/s vs. (4.53±1.22) m/s], V median [5.06 (4.48, 5.52) m/s vs. 4.35 (3.42, 4.96) m/s], V min [3.35 (2.68, 3.88) m/s vs. 2.59 (2.11, 3.34) m/s], the proportion of patients with axillary lymph node metastasis [56.5% (13/23) vs. 22.8% (21/92)], and the Ki-67 positivity index [35% (30%, 55%) vs. 25% (15%, 35%)] in the HER2 positive group were higher than those in the HER2 negative group, and the differences were statistically significant (all P < 0.05); There was no statistically significant difference in age, lesion location, pathological type, vascular invasion, nerve invasion and long diameter, short diameter, echo, regular shape, clear boundary, posterior echo, calcification, blood flow grading, Breast Imaging Report and Data System (BI-RADS) classification detected by ultrasound between the two groups (all P > 0.05). Multivariate logistic regression analysis showed that increased ultrasound V max ( OR = 1.786, 95% CI: 1.283-2.485, P = 0.001) and axillary lymph node metastasis ( OR = 4.185, 95% CI: 1.327-13.197, P = 0.015) and elevated Ki-67 positivity index ( OR = 1.042, 95% CI: 1.014-1.071, P = 0.003) were independent risk factors for HER2 positivity. ROC curve analysis showed that the area under the curve (AUC) of HER2 positive breast cancer which was difficult to be determined by IHC was 0.816 (95% CI: 0.732-0.883), that was higher than 0.712 (95% CI: 0.620-0.794) of V max, 0.601 (95% CI: 0.504-0.692) of axillary lymph node metastasis and 0.706 (95% CI: 0.613-0.788) of Ki-67 positivity index based on the nomogram constructed by the above independent risk factors, with statistically significant differences (all P < 0.05). The calibration curve showed that the predicted probability of the nomogram model was close to the actual probability, and DCA indicated that the clinical net benefit of the model was good. Conclusions:The nomogram constructed based on ultrasonic SWE parameter V max, axillary lymph node metastasis and Ki-67 positivity index has a good predictive effect on HER2 status of breast cancer which is difficult to be determined by IHC.

Objective:To explore the value of preoperative 18F-fluorodeoxyglucose ( 18F-FDG) positron emission tomography (PET) metabolic heterogeneity parameters in predicting tumor deposits (TD) in colorectal cancer (CRC). Methods:A retrospective analysis was conducted on 91 CRC patients who underwent surgical treatment at the Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School from February 2013 to March 2024. All patients underwent preoperative 18F-FDG PET/CT examination. The LIFEx-7.5.15 software was used to delineate the primary lesion with 40% of maximum standardized uptake value (SUV max) as the relative threshold, and metabolic parameters were extracted. Intratumoral metabolic heterogeneity parameters included cumulative SUV histogram area under the curve (AUC-CSH), heterogeneity index (HI), heterogeneity factor (HF), and coefficient of variation (CV). The presence of TD was confirmed by postoperative pathological examination. Differences in data between the TD group and non-TD (NTD) group were compared. Binary logistic regression analysis was used to identify independent risk factors for TD, and receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy of each parameter for TD. Results:Postoperative pathological diagnosis showed that 27 patients were included in the TD group and 64 in the NTD group. There were statistically significant differences between the TD group and NTD group in CV ( Z=-3.145, P=0.002) and the proportion of patients with carcinoembryonic antigen (CEA) >10 ng/ml (χ 2=10.751, P=0.001), while no statistically significant differences were found in HI, HF, or AUC-CSH (all P>0.05). Binary logistic regression analysis showed that CV was an independent risk factor for TD. ROC curve analysis showed that the area under the ROC curve (AUC) of CV for predicting TD was 0.709(95% CI: 0.593-0.826), which was higher than that of other metabolic heterogeneity parameters. Conclusions:The preoperative 18F-FDG PET/CT metabolic heterogeneity parameter CV has value in predicting TD in CRC patients.

Objective:To explore the value of preoperative 18F-fluorodeoxyglucose ( 18F-FDG) positron emission tomography (PET) metabolic heterogeneity parameters in predicting tumor deposits (TD) in colorectal cancer (CRC). Methods:A retrospective analysis was conducted on 91 CRC patients who underwent surgical treatment at the Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School from February 2013 to March 2024. All patients underwent preoperative 18F-FDG PET/CT examination. The LIFEx-7.5.15 software was used to delineate the primary lesion with 40% of maximum standardized uptake value (SUV max) as the relative threshold, and metabolic parameters were extracted. Intratumoral metabolic heterogeneity parameters included cumulative SUV histogram area under the curve (AUC-CSH), heterogeneity index (HI), heterogeneity factor (HF), and coefficient of variation (CV). The presence of TD was confirmed by postoperative pathological examination. Differences in data between the TD group and non-TD (NTD) group were compared. Binary logistic regression analysis was used to identify independent risk factors for TD, and receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy of each parameter for TD. Results:Postoperative pathological diagnosis showed that 27 patients were included in the TD group and 64 in the NTD group. There were statistically significant differences between the TD group and NTD group in CV ( Z=-3.145, P=0.002) and the proportion of patients with carcinoembryonic antigen (CEA) >10 ng/ml (χ 2=10.751, P=0.001), while no statistically significant differences were found in HI, HF, or AUC-CSH (all P>0.05). Binary logistic regression analysis showed that CV was an independent risk factor for TD. ROC curve analysis showed that the area under the ROC curve (AUC) of CV for predicting TD was 0.709(95% CI: 0.593-0.826), which was higher than that of other metabolic heterogeneity parameters. Conclusions:The preoperative 18F-FDG PET/CT metabolic heterogeneity parameter CV has value in predicting TD in CRC patients.

Objective:To investigate the predictive value of 18F-FDG PET related metabolic parameters on microsatellite instability-high (MSI-H) and human epidermal growth factor receptor-2(HER2) expression in colorectal cancer (CRC). Methods:The 18F-FDG PET imaging, clinical and pathological data of 101 CRC patients (58 males, 43 females; age 68.0(58.0, 75.0) years) admitted to Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University from January 2016 to March 2024 were retrospectively collected, including 17 cases in MSI-H group, 84 cases in microsatellite stability/microsatellite instability-low (MSS/MSI-L) group, 23 cases in HER2 expression group, 34 cases in non-HER2 expression group, and 44 patients without HER2 detection. Univariate analyses (independent-sample t test, Mann-Whitney U test, χ2 test) and multivariate logistic regression analysis were used to screen out independent risk factors, and ROC curve was used to evaluate the predictive efficacy. Bootstrap method was used to verify the model internally. Results:There were significant differences in metabolic tumor volume (MTV) 80% between MSI-H group and MSS/MSI-L group (2.1(1.6, 4.0) vs 1.4(1.0, 2.7) cm 3;Z=-2.10, P=0.036), and total lesion glycolysis (TLG) 80% and carcinoembryonic antigen (CEA) were significantly different between those 2 groups ( Z=-2.27, χ2=6.40, both P<0.05). There were significant differences in TLG 80% (29.0(16.1, 41.0) vs 14.3(9.4, 22.9) g; Z=-2.80, P=0.005) between HER2 expression group and non-HER2 expression group, and significant differences were also found in MTV 80%, heterogeneity index (HI) and CV ( Z=-2.24, t values: -2.26, 2.54, all P<0.05). The independent risk factors for MSI-H were MTV 80% (odds ratio ( OR)=1.326, 95% CI: 1.015-1.733, P=0.038) and CEA ( OR=0.200, 95% CI: 0.056-0.706, P=0.012), with the AUC for the combined model of 0.730 (95% CI: 0.605-0.856), and the concordance index (C-index) of 0.716. The independent risk factors for HER2 expression were TLG 80% ( OR=1.037, 95% CI: 1.001-1.073, P=0.041) and CV ( OR=1.467, 95% CI: 1.073-2.005, P=0.016), with the AUC for the combined model of 0.775 (95% CI: 0.645-0.875), and the C-index of 0.757. Conclusions:18F-FDG PET can be used as a noninvasive tool to evaluate CRC microsatellite status and HER2 gene amplification. MTV 80% and CEA are independent risk factors for MSI-H; TLG 80% and CV are independent risk factors for HER2 expression.

Objective:To investigate the predictive value of conventional ultrasound，intratumoral and peritumoral radiomics models based on ultrasound images for the efficacy of neoadjuvant therapy in triple negative breast cancer（TNBC），and to construct a combined model.Methods:A total of 122 patients diagnosed with TNBC and admitted to Nanjing Drum Tower Hospital，Affiliated Hospital of Medical School，Nanjing University between April 2020 and December 2023 were retrospectively selected as study subjects. All patients underwent surgical resection after neoadjuvant therapy and were categorized into two groups：50 cases in the pathological complete remission（pCR）group and 72 cases in the non-pCR group according to surgical pathology. A comprehensive array of clinical data，along with conventional ultrasound imaging findings，was systematically collected from patients prior to treatment and at the conclusion of the second cycle of treatment. The region of interest（ROI）was delineated on the baseline two-dimensional gray-scale ultrasound image using 3D Slicer software on the maximum long-axis section of the lesion. The peri-tumor area was obtained by circularly expanding outward by 5 mm，and the imaging histological features were extracted separately. The dataset was then randomly partitioned into a training set and a validation set，with a ratio of 7∶3. The χ2/ t/Mann-Whitney U tests were used for intergroup comparison of general information. Maximum correlation minimum redundancy and least absolute shrinkage and selection operator regression were used to screen the optimal radiomics feature set，respectively. Variables that exhibited statistically significant differences between group comparisons were then employed to construct combined models，incorporating intratumor and peritumor ultrasonographic models. The predictive efficacy，accuracy，and clinical utility of the models were assessed using ROC curves，calibration curves，and decision curve analysis（DCA），respectively. Results:Subsequent between-group comparisons and multifactorial Logistic regression analysis identified blood flow（ OR=0.213，95% CI=0.062-0.735）and change rate of length diameter（ OR=1.091，95% CI=1.013-1.175）as independent risk factors for predicting pCR. A total of seven and eight radiomics features from each of the intratumoral and peritumoral regions were screened for the construction of intratumoral imaging histology score（RS）and peritumoral RS. The Nomogram model was constructed by combining the blood flow，change rate of length diameter，intratumoral RS，and peritumoral RS，and its AUC values in the training and validation sets were 0.884（95% CI=0.815-0.953）and 0.841（95% CI=0.683-0.940），respectively. The calibration curves demonstrated the Nomogram model's exceptional precision，with a C-index of 0.860 and 0.782 for the training and validation sets，respectively. The DCA revealed that the Nomogram model exhibited the optimal net clinical benefit. Conclusions:Conventional ultrasound，intratumoral and peritumoral radiomics models based on ultrasound images have been shown to possess satisfactory predictive value for the efficacy of neoadjuvant therapy in TNBC，thereby facilitating clinical decision-making.

Objective:To explore the predictive value of ultrasonic shear wave elastography (SWE) parameters and pathological characteristics on the status of human epidermal growth factor receptor 2 (HER2), which is difficult to be determined by immunohistochemistry (IHC) in breast cancer, and to construct a nomogram model.Methods:A retrospective case-control study was conducted. One hundred and fifteen cases of breast cancer diagnosed and treated in Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University from September 2018 to April 2022 were selected, and their HER2 was evaluated as IHC 2+; the HER2 expression status was determined by fluorescence in situ hybridization (FISH) detection, including 23 HER2 positive cases and 92 HER2 negative cases. The ultrasound SWE parameters [including maximum shear wave velocity (V max), mean shear wave velocity (V mean), median shear wave velocity (V median), minimum shear wave velocity (V min)] and clinicopathological characteristics between HER2 positive and negative groups were compared. The variables with statistically significant differences ( P < 0.05) between groups were included in a multivariate logistic regression model, the independent risk factors for HER2 positivity were screened, and a nomogram model was constructed based on these independent risk factors. With the FISH test results as the gold standard, the efficacy of nomogram in judging HER2 positivity in breast cancer which was difficult to be identified by IHC was evaluated with the receiver operating characteristic (ROC) curve; the accuracy and clinical net benefit of the nomogram model were evaluated using calibration curve and decision curve analysis (DCA), respectively. Results:The patients were all female, aged (56±13) years, ranging from 30 to 88 years old. V max [ M ( Q1, Q3)] [8.54 (7.38, 9.47) m/s vs. 6.46 (5.07, 8.42) m/s], V mean [(5.41±0.78) m/s vs. (4.53±1.22) m/s], V median [5.06 (4.48, 5.52) m/s vs. 4.35 (3.42, 4.96) m/s], V min [3.35 (2.68, 3.88) m/s vs. 2.59 (2.11, 3.34) m/s], the proportion of patients with axillary lymph node metastasis [56.5% (13/23) vs. 22.8% (21/92)], and the Ki-67 positivity index [35% (30%, 55%) vs. 25% (15%, 35%)] in the HER2 positive group were higher than those in the HER2 negative group, and the differences were statistically significant (all P < 0.05); There was no statistically significant difference in age, lesion location, pathological type, vascular invasion, nerve invasion and long diameter, short diameter, echo, regular shape, clear boundary, posterior echo, calcification, blood flow grading, Breast Imaging Report and Data System (BI-RADS) classification detected by ultrasound between the two groups (all P > 0.05). Multivariate logistic regression analysis showed that increased ultrasound V max ( OR = 1.786, 95% CI: 1.283-2.485, P = 0.001) and axillary lymph node metastasis ( OR = 4.185, 95% CI: 1.327-13.197, P = 0.015) and elevated Ki-67 positivity index ( OR = 1.042, 95% CI: 1.014-1.071, P = 0.003) were independent risk factors for HER2 positivity. ROC curve analysis showed that the area under the curve (AUC) of HER2 positive breast cancer which was difficult to be determined by IHC was 0.816 (95% CI: 0.732-0.883), that was higher than 0.712 (95% CI: 0.620-0.794) of V max, 0.601 (95% CI: 0.504-0.692) of axillary lymph node metastasis and 0.706 (95% CI: 0.613-0.788) of Ki-67 positivity index based on the nomogram constructed by the above independent risk factors, with statistically significant differences (all P < 0.05). The calibration curve showed that the predicted probability of the nomogram model was close to the actual probability, and DCA indicated that the clinical net benefit of the model was good. Conclusions:The nomogram constructed based on ultrasonic SWE parameter V max, axillary lymph node metastasis and Ki-67 positivity index has a good predictive effect on HER2 status of breast cancer which is difficult to be determined by IHC.

Objective:To investigate the value of conventional ultrasound combined with shear wave elastography (SWE) in the differential diagnosis of non-mass ductal carcinoma in situ (DCIS) and invasive breast cancer (IBC) . Methods:A total of 102 patients with non-mass breast cancer admitted to Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School from March 2019 to April 2022 were selected as the study objects, including 32 cases of DCIS and 70 cases of IBC. Conventional ultrasound parameters echo, microcalcification, location, posterior echo, blood flow, axillary lymph node, breast imaging reporting and data system (BI-RADS) score and SWE-related parameters maximum shear wave velocity (SWV max), minimum shear wave velocity (SWV min), mean shear wave velocity (SWV mean) and median shear wave velocity (SWV median) were compared between patients with non-mass DCIS and IBC. Binary logistic regression was used to analyze the independent factors for the differential diagnosis of non-mass DCIS and IBC. Based on the results of multivariate analysis, a nomogram prediction model was constructed and the predictive efficacy of the prediction model was evaluated by receiver operator characteristic (ROC) curve. Calibration curve and decision curve analysis (DCA) were used to evaluate the accuracy and practicability of the model. Results:There were statistically significant differences in blood flow ( χ2=8.47, P=0.004), axillary lymph nodes ( χ2=9.11, P=0.003), SWV max ( Z=-3.32, P<0.001), SWV mean ( t=3.00, P=0.003), SWV median ( Z=-2.69, P=0.007) between patients with non-mass DCIS and IBC. Multivariate analysis showed that, blood flow ( OR=3.56, 95% CI: 1.28-9.89, P=0.015), axillary lymph nodes ( OR=3.04, 95% CI: 1.10-8.42, P=0.032) and SWV max ( OR=1.40, 95% CI: 1.13-1.73, P=0.002) were independent factors for distinguishing non-mass DCIS from IBC. A nomogram prediction model was constructed based on blood flow, axillary lymph nodes and SWV max. ROC curve analysis showed that, the area under the curve of blood flow, axillary lymph nodes, SWV max, and prediction model for differential diagnosis of non-mass DCIS and IBC were 0.64 (95% CI: 0.52-0.76), 0.66 (95% CI: 0.55-0.77), 0.71 (95% CI: 0.60-0.81), and 0.79 (95% CI: 0.70-0.88), respectively, and the differential diagnostic value of prediction model was higher than that of blood flow ( Z=2.92, P=0.004), axillary lymph nodes ( Z=2.94, P=0.003), and SWV max ( Z=1.88, P=0.060) alone. The C-index of the prediction model for the differential diagnosis of non-mass DCIS and IBC was 0.77, and the calibration curve showed that the prediction probability of the prediction model was close to the actual probability. DCA showed that this prediction model could provide higher clinical net benefit and had certain clinical practicability. Conclusion:Blood flow and axillary lymph nodes in conventional ultrasound parameters and SWV max of SWE-related parameters are independent factors in the differential diagnosis of non-mass DCIS and IBC. The nomogram prediction model constructed by this method has a high value in the differential diagnosis of non-mass DCIS and IBC.