A multi-parameter intrahepatic cholangiocarcinoma scoring system based on modified contrast-enhanced ultrasound LI-RADS M criteria for differentiating intrahepatic cholangiocarcinoma from hepatocellular carcinoma.

PURPOSE: To develop a multi-parameter intrahepatic cholangiocarcinoma (ICC) scoring system and compare its diagnostic performance with contrast-enhanced ultrasound (CEUS) liver imaging reporting and data system M (LR-M) criteria for differentiating ICC from hepatocellular carcinoma (HCC). METHODS: This retrospective study enrolled 62 high-risk patients with ICCs and 62 high-risk patients with matched HCCs between January 2022 and December 2022 from two institutions. The CEUS LR-M criteria was modified by adjusting the early wash-out onset (within 45 s) and the marked wash-out (within 3 min). Then, a multi-parameter ICC scoring system was established based on clinical features, B-mode ultrasound features, and modified LR-M criteria. RESULT: We found that elevated CA 19-9 (OR=12.647), lesion boundary (OR=11.601), peripheral rim-like arterial phase hyperenhancement (OR=23.654), early wash-out onset (OR=7.211), and marked wash-out (OR=19.605) were positive predictors of ICC, whereas elevated alpha-fetoprotein (OR=0.078) was a negative predictor. Based on these findings, an ICC scoring system was established. Compared with the modified LR-M and LR-M criteria, the ICC scoring system showed the highest area under the curve (0.911 vs. 0.831 and 0.750, both p<0.05) and specificity (0.935 vs. 0.774 and 0.565, both p<0.05). Moreover, the numbers of HCCs categorized as LR-M decreased from 27 (43.5%) to 14 (22.6%) and 4 (6.5%) using the modified LR-M criteria and ICC scoring system, respectively. CONCLUSION: The modified LR-M criteria-based multi-parameter ICC scoring system had the highest specificity for diagnosing ICC and reduced the number of HCC cases diagnosed as LR-M category.

Assessment of the Diagnostic Performance of Clinical Examinations and High-Frequency Ultrasound in Patients With Pigmented Skin Tumors.

OBJECTIVES: To investigate whether the integration of high-frequency ultrasound (HFUS) to routine clinical examinations could improve diagnostic performance and management decision for pigmented skin tumors. METHODS: Three general practitioners trained previously and a dermatologist independently assessed pigmented skin tumors and rendered management decision based on clinical examinations alone or clinical examinations integrating HFUS. RESULTS: After integrating HFUS, the diagnostic area under the curve (AUC) (0.658-0.693 versus 0.848, all P < .05) and specificity (46.6-58.6% versus 89.7%, all P < .05) for pigmented skin malignancies were improved for general practitioners, meanwhile unnecessary biopsy rate reduced (42.9-53.6% versus 10.7%, P < .001). To the dermatologist, the diagnostic AUC (0.822 versus 0.949, P < .001), sensitivity (81.7% versus 96.7%, P = .012) and specificity (0.828 versus 0.931, P = .031) improved significantly, meanwhile both missed biopsy rate (14.5% versus 4.8%, P = .031) and unnecessary biopsy rate (19.6% versus 7.1%, P = .016) decreased. Additionally, the diagnostic performance of the general practitioner with integrating HFUS could be comparable with the dermatologist based on clinical examinations alone (all P > .05). CONCLUSIONS: As a complementary tool of clinical examinations, HFUS could help physicians differentiate pigmented skin malignancies and manage decision.

Quantitative analysis of pre-treatment dynamic contrast-enhanced ultrasound for assessing the response of colorectal liver metastases to chemotherapy plus targeted therapy: a dual-institutional study.

OBJECTIVES: To investigate the clinical value of pre-treatment quantitative contrast-enhanced ultrasound (CEUS) in assessing the response of colorectal liver metastases (CRLM) to chemotherapy plus targeted therapy. METHODS: This study retrospectively enrolled 50 CRLM patients from the Zhongshan Hospital, Fudan University as the training cohort and 14 patients from Shanghai Tenth People's Hospital as the testing cohort. Patients underwent the CEUS examination before receiving chemotherapy (CAPOX, FOLFOX, FOLFIRI, or FOLFOXIRI) plus targeted therapy (Bevacizumab or Cetuximab). The therapy response was determined according to Response Evaluation Criteria in Solid Tumors version 1.1 based on pre-treatment CT and 3-month follow-up CT after therapy. Dynamic analysis was performed by VueBox® software. Time-intensity curves with quantitative perfusion parameters were obtained. In the training cohort, univariable and multivariable logistic regression analyses were used to develop the predictive model of therapy response. The predictive performance of the developed model was validated in the testing cohort. RESULTS: After the logistic regression analyses, the peak enhancement (PE) (odds ratio = 1.640; 95% confidence intervals [CI] 1.022-2.633) and time to peak (TTP) (odds ratio = 0.495; 95% CI 0.246-0.996) were determined as independent predictive factors. PE and TTP generated from VueBox® were not affected by ultrasound instruments and contrast agent dosage in therapy response evaluation (P > 0.05). The logistic regression model achieved satisfactory prediction performance (area under the curve: 0.923 in the training cohort and 0.854 in the testing cohort). CONCLUSION: CEUS with dynamic quantitative perfusion analysis, which presents high consistency, has potential practical value in predicting the response of CRLM to chemotherapy plus targeted therapy.

Response Evaluation Using Contrast-Enhanced Ultrasound for Unresectable Advanced Hepatocellular Carcinoma Treated With Tyrosine Kinase Inhibitors Plus Anti-PD-1 Antibody Therapy.

OBJECTIVE: The aim of the work described here was to evaluate the role of contrast-enhanced ultrasound (CEUS) in response evaluation for unresectable advanced hepatocellular carcinoma (HCC) treated with tyrosine kinase inhibitors (TKIs) plus anti-programmed cell death protein-1 (PD-1) antibody therapy. METHODS: A prospective cohort of consecutive patients with HCC who received combined TKI/anti-PD-1 antibody treatment for unresectable HCC between January 2022 and October 2022 was included in this study. The patients underwent unenhanced ultrasound (US) and CEUS examinations before treatment and at follow-up. Changes in the largest diameters of the target tumor on unenhanced US and the largest diameters of the enhancing target tumors on CEUS were evaluated. Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 with unenhanced US and magnetic resonance imaging/computed tomography (MRI/CT) and modified RECIST (mRECIST) with CEUS and CEMRI/CT were used to assess treatment response. RESULTS: A total of 24 HCC patients (23 men and 1 woman; mean age: 56.5 ± 8.5 y; Barcelona Clinic Liver Cancer stage C, 62.5%; 29 intrahepatic target tumors) were studied. Calculations of degree of necrosis in the target tumors revealed no significant differences between CEUS and CEMRI/CT (44.5 ± 36.2% vs. 45.3 ± 36.8%, p = 0.862). As for the differentiation of responders from non-responders, the agreement between RECIST version 1.1 of unenhanced US and mRECIST-CEUS was poor (κ coefficient = 0.233). Meanwhile, there was a high degree of concordance between mRECIST-CEUS and mRECIST-CEMRI/CT (κ coefficient = 0.812). CONCLUSION: CEUS proved to be superior to baseline US and is comparable to CEMRI/CT in defining treatment outcome for combined TKI/anti-PD-1 antibody therapy.

Diagnostic value of high-frequency ultrasound (HFUS) in evaluation of subcutaneous lesions.

BACKGROUND: It is unknown whether high-frequency ultrasound (HFUS) can evaluate invisible subcutaneous lesions. We aimed to investigate the diagnostic value of HFUS in invisible subcutaneous lesions. METHOD: Patients with invisible subcutaneous lesions were prospectively recruited from two centres. Before undergoing biopsy or surgery, each lesion was independently evaluated by two clinicians. One provides a clinical diagnosis by only clinical examination and the other provides an integrated diagnosis by combining clinical examination and HFUS information. Diagnoses were classified as correct, wrong, and indeterminate. A total of 391 lesions from 355 patients were enrolled, including 225 epidermoid cysts, 77 lipomas, 25 pilomatrixomas, 21 haemangiomas, 19 dermatofibromas, 11 dermatofibrosarcoma protuberans (DFSP), 7 neurofibromas, and 6 leiomyomas. Using pathological results as the gold standard, diagnostic performance was compared. RESULTS: The number of correct diagnoses increased from 185 (47.3%) by clinical examination alone to 316 (80.8%) after the addition of HFUS (P < 0.05). Meanwhile, the indeterminate diagnosis rate decreased from 143 (36.6%) to 10 (2.6%). Using HFUS, the accuracy improved significantly for epidermoid cysts (59.6% vs. 86.7%), lipomas (50.6% vs. 94.8%), pilomatrixomas (0% vs. 48.0%), haemangiomas (23.8% vs. 57.1%), and DFSPs (0% vs. 81.8%) (all p < 0.05). However, HFUS did not significantly improve the diagnostic accuracy of dermatofibromas (15.8% vs. 21.1%, p > 0.999), neurofibromas (42.9% vs. 71.4%, p = 0.625), or leiomyomas (16.7% vs. 100%, p = 0.063). CONCLUSION: Combining HFUS and clinical examination can generally improve the diagnostic accuracy and decrease the indeterminacy of invisible subcutaneous lesions, especially epidermoid cysts, lipomas, pilomatrixomas, haemangiomas, and DFSPs. However, for some rare lesions, HFUS cannot provide useful information.

Risk stratification of gallbladder masses by machine learning-based ultrasound radiomics models: a prospective and multi-institutional study.

OBJECTIVE: This study aimed to evaluate the diagnostic performance of machine learning (ML)-based ultrasound (US) radiomics models for risk stratification of gallbladder (GB) masses. METHODS: We prospectively examined 640 pathologically confirmed GB masses obtained from 640 patients between August 2019 and October 2022 at four institutions. Radiomics features were extracted from grayscale US images and germane features were selected. Subsequently, 11 ML algorithms were separately used with the selected features to construct optimum US radiomics models for risk stratification of the GB masses. Furthermore, we compared the diagnostic performance of these models with the conventional US and contrast-enhanced US (CEUS) models. RESULTS: The optimal XGBoost-based US radiomics model for discriminating neoplastic from non-neoplastic GB lesions showed higher diagnostic performance in terms of areas under the curves (AUCs) than the conventional US model (0.822-0.853 vs. 0.642-0.706, p < 0.05) and potentially decreased unnecessary cholecystectomy rate in a speculative comparison with performing cholecystectomy for lesions sized over 10 mm (2.7-13.8% vs. 53.6-64.9%, p < 0.05) in the validation and test sets. The AUCs of the XGBoost-based US radiomics model for discriminating carcinomas from benign GB lesions were higher than the conventional US model (0.904-0.979 vs. 0.706-0.766, p < 0.05). The XGBoost-US radiomics model performed better than the CEUS model in discriminating GB carcinomas (AUC: 0.995 vs. 0.902, p = 0.011). CONCLUSIONS: The proposed ML-based US radiomics models possess the potential capacity for risk stratification of GB masses and may reduce the unnecessary cholecystectomy rate and use of CEUS. CLINICAL RELEVANCE STATEMENT: The machine learning-based ultrasound radiomics models have potential for risk stratification of gallbladder masses and may potentially reduce unnecessary cholecystectomies. KEY POINTS: • The XGBoost-based US radiomics models are useful for the risk stratification of GB masses. • The XGBoost-based US radiomics model is superior to the conventional US model for discriminating neoplastic from non-neoplastic GB lesions and may potentially decrease unnecessary cholecystectomy rate for lesions sized over 10 mm in comparison with the current consensus guideline. • The XGBoost-based US radiomics model could overmatch CEUS model in discriminating GB carcinomas from benign GB lesions.

Three-dimensional convolutional neural network model to identify clinically significant prostate cancer in transrectal ultrasound videos: a prospective, multi-institutional, diagnostic study.

Background: Identifying patients with clinically significant prostate cancer (csPCa) before biopsy helps reduce unnecessary biopsies and improve patient prognosis. The diagnostic performance of traditional transrectal ultrasound (TRUS) for csPCa is relatively limited. This study was aimed to develop a high-performance convolutional neural network (CNN) model (P-Net) based on a TRUS video of the entire prostate and investigate its efficacy in identifying csPCa. Methods: Between January 2021 and December 2022, this study prospectively evaluated 832 patients from four centres who underwent prostate biopsy and/or radical prostatectomy. All patients had a standardised TRUS video of the whole prostate. A two-dimensional CNN (2D P-Net) and three-dimensional CNN (3D P-Net) were constructed using the training cohort (559 patients) and tested on the internal validation cohort (140 patients) as well as on the external validation cohort (133 patients). The performance of 2D P-Net and 3D P-Net in predicting csPCa was assessed in terms of the area under the receiver operating characteristic curve (AUC), biopsy rate, and unnecessary biopsy rate, and compared with the TRUS 5-point Likert score system as well as multiparametric magnetic resonance imaging (mp-MRI) prostate imaging reporting and data system (PI-RADS) v2.1. Decision curve analyses (DCAs) were used to determine the net benefits associated with their use. The study is registered at https://www.chictr.org.cn with the unique identifier ChiCTR2200064545. Findings: The diagnostic performance of 3D P-Net (AUC: 0.85-0.89) was superior to TRUS 5-point Likert score system (AUC: 0.71-0.78, P = 0.003-0.040), and similar to mp-MRI PI-RADS v2.1 score system interpreted by experienced radiologists (AUC: 0.83-0.86, P = 0.460-0.732) and 2D P-Net (AUC: 0.79-0.86, P = 0.066-0.678) in the internal and external validation cohorts. The biopsy rate decreased from 40.3% (TRUS 5-point Likert score system) and 47.6% (mp-MRI PI-RADS v2.1 score system) to 35.5% (2D P-Net) and 34.0% (3D P-Net). The unnecessary biopsy rate decreased from 38.1% (TRUS 5-point Likert score system) and 35.2% (mp-MRI PI-RADS v2.1 score system) to 32.0% (2D P-Net) and 25.8% (3D P-Net). 3D P-Net yielded the highest net benefit according to the DCAs. Interpretation: 3D P-Net based on a prostate grayscale TRUS video achieved satisfactory performance in identifying csPCa and potentially reducing unnecessary biopsies. More studies to determine how AI models better integrate into routine practice and randomized controlled trials to show the values of these models in real clinical applications are warranted. Funding: The National Natural Science Foundation of China (Grants 82202174 and 82202153), the Science and Technology Commission of Shanghai Municipality (Grants 18441905500 and 19DZ2251100), Shanghai Municipal Health Commission (Grants 2019LJ21 and SHSLCZDZK03502), Shanghai Science and Technology Innovation Action Plan (21Y11911200), and Fundamental Research Funds for the Central Universities (ZD-11-202151), Scientific Research and Development Fund of Zhongshan Hospital of Fudan University (Grant 2022ZSQD07).

The Value of Ultrasound for Differentiating Trichilemmal Cysts From Epidermoid Cysts.

OBJECTIVES: This study was aimed to evaluate the diagnostic performance of ultrasound (US) in differentiating trichilemmal cysts (TCs) from epidermoid cysts (ECs). METHODS: Based on clinical and ultrasound features, a prediction model was established and validated. 164 cysts in the pilot cohort and another 69 in the validation cohort diagnosed with TCs or ECs histopathologically were evaluated. The same radiologist performed all ultrasound examinations. RESULTS: For clinic features, TCs tended to occur in females compared with ECs (66.7 vs 28.5%; P < .001). In addition, TCs were prone to occur in the hairy area compared with ECs (77.8 vs 13.1%; P < .001). For ultrasound features, the internal hyperechogenicity and cystic change were more likely to appear in TCs in comparison with ECs (92.6 vs 25.5%; P < .001; 70.4 vs 23.4%; P < .001, respectively). Upon the features mentioned above, a prediction model was established with the areas under the receiver operating characteristic curves of 0.936 and 0.864 in the pilot and validation cohorts, respectively. CONCLUSIONS: US is promising for differentiating TCs from ECs and is valuable for their clinical management.

The Value of Ultra-High-Frequency Ultrasound for the Differentiation between Superficial Basal Cell Carcinoma and Bowen's Disease.

BACKGROUND: The similar visual appearance of superficial basal cell carcinoma (sBCC) and Bowen's disease (BD) may cause confusion for diagnosis. OBJECTIVE: The aim of the study was to investigate the value of ultra-high-frequency ultrasound (uHFUS) in differentiating sBCC from BD. MATERIALS AND METHODS: This prospective study included a pilot cohort of 110 patients (73 BDs and 37 sBCCs) from November 2016 to October 2020 and a validation cohort of 42 patients (30 BDs and 12 sBCCs) from July 2021 to December 2021. Clinical and uHFUS features of pathologically confirmed sBCC and BD were assessed. A predictive model was developed based on the uHFUS features of the pilot cohort. Subsequently, the model was validated and compared with clinical diagnosis in the validation cohort. RESULTS: uHFUS features with significant differences between sBCC and BD included lesion surface, skin layer involvement, hyperkeratosis, and hyperechoic spots (all p < 0.05). A prediction model based on the above features was established to identify sBCC and BD in the pilot and validation cohorts with areas under the curve (AUC) of 0.908 and 0.923, sensitivity of 82.3% and 83.3%, specificity of 91.9% and 91.7%, and accuracy of 85.5% and 85.7%, respectively, which were significantly higher than those obtained by clinical diagnosis based on photographic pictures of lesions, with the AUC of 0.692, sensitivity of 63.3%, specificity of 75.3%, and accuracy of 66.7% (all p < 0.05). CONCLUSION: uHFUS provides detailed internal features of sBCC and BD, which facilitates the differentiation between sBCC and BD, and its diagnostic performance is superior to clinical diagnosis.

High-Frequency Ultrasound for Evaluation of the Pathological Invasion Level of Extramammary Paget Disease.

OBJECTIVES: Pathological invasion level of extramammary Paget disease (EMPD) is strongly related with its risk staging, treatment, and prognosis. However, the current evaluation before treatments fails to evaluate pathological invasion level of EMPD. High-frequency ultrasound (HFUS) may play a key role to solve this problem. The purpose was to explore the performance of HFUS in the evaluation of pathological invasion level of EMPD. METHODS: Sixty pathologically proven EMPD patients were retrospectively enrolled and divided into 2 groups as follows: in situ in the epidermis (IE) (n = 42) and invasion into the dermis or subcutaneous (ID) (n = 18) groups. Clinical and HFUS features were compared between the 2 groups. RESULTS: Between the 2 groups, HFUS features (lesion shape, internal echogenicity and echotexture, surface shape, epidermal hyperechoic layer on the surface, the "pseudopod sign", and color Doppler ultrasound features) and clinical features were comparable (all P >.05). Tumor growth pattern significantly differed between the 2 groups (P <.05). Infiltration depth was significantly deeper for the ID group than the IE group (P <.05). With a cutoff value of 1.55 mm for infiltration depth, the area under the receiver operating characteristic curve was 0.833. CONCLUSIONS: HFUS features of tumor growth pattern and infiltration depth may contribute to the assessment of invasion level of EMPD.

High-frequency ultrasound for differentiation between high-risk basal cell carcinoma and cutaneous squamous cell carcinoma.

BACKGROUND: The similar visual appearance of high-risk basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC) may cause confusion for diagnosis. High-frequency ultrasound (HFUS) may provide additional intralesional information and thus help to distinguish them. METHOD: In this retrospective study, we analyzed the clinical characteristics, HFUS grayscale, and color Doppler flow imaging (CDFI) features of pathologically confirmed high-risk BCC and cSCC lesions (n = 65 vs n = 68). Subsequently, discrimination models based on the significant HFUS features were established. RESULTS: Between high-risk BCC and cSCC lesions, the HFUS grayscale features of the lesion size (10.0 mm vs 17.4 mm), thickness (3.1 mm vs 5.9 mm), internal hyperechoic spots (80.0% vs 23.5%), and posterior acoustic shadowing (16.9% vs 66.2%) were statistically different (all p < 0.001). As for the CDFI features, high-risk BCC lesions mainly appeared as pattern II (47.7%), while cSCC lesions mainly appeared as pattern III (66.2%). Based on the above five features, an optimal discrimination model was established with a sensitivity of 91.2%, a specificity of 87.7%, and an accuracy of 89.5%. CONCLUSION: HFUS features, including size, thickness, internal hyperechoic spots, posterior acoustic shadowing, and Doppler vascularity pattern, are useful for differential diagnosis between high-risk BCC and cSCC.

Quantitative assessment of crystal dissolution in gout during urate-lowering therapy with computer-aided MicroPure imaging: a cohort study.

BACKGROUND: To evaluate whether MicroPure imaging, an ultrasound (US) image-processing technique with computer-aided analysis, can quantitatively detect crystal dissolution during urate-lowering therapy (ULT) in gout. METHODS: This was a prospective study of gout patients requiring ULT. The first metatarsophalangeal joints were examined using US and MicroPure before and after 3 months of ULT. Elementary lesions of gout, including the double contour sign (DCS), aggregates, tophi, erosion, and other US features were recorded at baseline and 3 months. MicroPure imaging features were automatically calculated by a self-developed software. Patients were divided into goal-achieved and goal-not-achieved groups according to their urate levels at 3 months. The US and MicroPure imaging features of the two groups were analyzed at baseline and 3 months. RESULTS: A total of 55 consecutive patients were enrolled (25: goal-achieved group; 30: goal-not-achieved group). US findings demonstrated that the power Doppler signal grade decreased at 3 months, regardless of the group (both P<0.05). From baseline to 3 months, tophi size and the DCS reduced in the goal-achieved group (both P<0.05), while the US aggregate features showed no difference (P=0.250). However, on the MicroPure imaging, the number and density of aggregates at 3 months decreased in the goal-achieved group (both P<0.05). There were no significant changes at 3 months in any of the MicroPure imaging features in the goal-not-achieved group (all P>0.05). CONCLUSIONS: In comparison with B-mode US, computer-aided MicroPure imaging can sensitively and quantitatively detect aggregate dissolution during effective ULT after only 3 months of treatment.

Decoding the molecular subtypes of breast cancer seen on multimodal ultrasound images using an assembled convolutional neural network model: A prospective and multicentre study.

BACKGROUND: Preoperative determination of breast cancer molecular subtypes facilitates individualized treatment plan-making and improves patient prognosis. We aimed to develop an assembled convolutional neural network (ACNN) model for the preoperative prediction of molecular subtypes using multimodal ultrasound (US) images. METHODS: This multicentre study prospectively evaluated a dataset of greyscale US, colour Doppler flow imaging (CDFI), and shear-wave elastography (SWE) images in 807 patients with 818 breast cancers from November 2016 to February 2021. The St. Gallen molecular subtypes of breast cancer were confirmed by postoperative immunohistochemical examination. The monomodal ACNN model based on greyscale US images, the dual-modal ACNN model based on greyscale US and CDFI images, and the multimodal ACNN model based on greyscale US and CDFI as well as SWE images were constructed in the training cohort. The performances of three ACNN models in predicting four- and five-classification molecular subtypes and identifying triple negative from non-triple negative subtypes were assessed and compared. The performance of the multimodal ACNN was also compared with preoperative core needle biopsy (CNB). FINDING: The performance of the multimodal ACNN model (macroaverage area under the curve [AUC]: 0.89-0.96) was superior to that of the dual-modal ACNN model (macroaverage AUC: 0.81-0.84) and the monomodal ACNN model (macroaverage AUC: 0.73-0.75) in predicting four-classification breast cancer molecular subtypes, which was also better than that of preoperative CNB (AUC: 0.89-0.99 vs. 0.67-0.82, p < 0.05). In addition, the multimodal ACNN model outperformed the other two ACNN models in predicting five-classification molecular subtypes (AUC: 0.87-0.94 vs. 0.78-0.81 vs. 0.71-0.78) and identifying triple negative from non-triple negative breast cancers (AUC: 0.934-0.970 vs. 0.688-0.830 vs. 0.536-0.650, p < 0.05). Moreover, the multimodal ACNN model obtained satisfactory prediction performance for both T1 and non-T1 lesions (AUC: 0.957-0.958 and 0.932-0.985). INTERPRETATION: The multimodal US-based ACNN model is a potential noninvasive decision-making method for the management of patients with breast cancer in clinical practice. FUNDING: This work was supported in part by the National Natural Science Foundation of China (Grants 81725008 and 81927801), Shanghai Municipal Health Commission (Grants 2019LJ21 and SHSLCZDZK03502), and the Science and Technology Commission of Shanghai Municipality (Grants 19441903200, 19DZ2251100, and 21Y11910800).

Value of High-Frequency Ultrasound for Differentiating Invasive Basal Cell Carcinoma from Non-invasive Types.

The purpose of the study was to evaluate the value of high-frequency ultrasound (HFUS) for differentiating invasive basal cell carcinomas (BCCs) from non-invasive BCCs. We established a prediction model based on ultrasound features and validated it further. One hundred patients in the pilot cohort and another 43 in the validation cohort were evaluated. All patients underwent HFUS examinations by the same radiologist, and then were divided on the basis of pathology into invasive and non-invasive types. With respect to growth pattern, 60.5% of invasive BCCs had an irregular pattern, whereas 89.5% of non-invasive BCCs had a nodular or crawling pattern (p < 0.001). As for the layers involved, the more invasive BCCs broke through the dermis compared with non-invasive BCCs (23.3% vs. 1.8%) (p < 0.001). With respect to intralesional hyperechoic spot distribution, invasive and non-invasive BCCs tended to be clustered and absent/scattered-like, respectively (55.8% vs. 91.2%) (p < 0.001). On the basis of the aforementioned features, a prediction model was established with accuracies of 84.0% and 76.7%, respectively, in the pilot and validation cohorts. HFUS holds promise for the differentiation of the invasiveness of BCCs and is helpful in its clinical management.

High-frequency ultrasound in the diagnosis of the spectrum of cutaneous squamous cell carcinoma: Noninvasively distinguishing actinic keratosis, Bowen's Disease, and invasive squamous cell carcinoma.

OBJECTIVE: To evaluate high-frequency ultrasound (HFUS) features for diagnosing cutaneous squamous cell carcinoma (cSCC) as a spectrum of progressively advanced malignancies, including precursor actinic keratosis (AK), Bowen's disease (BD), and invasive squamous cell carcinoma (iSCC). METHOD: In this retrospective study, 160 skin lesions diagnosed histopathologically (54 AK, 54 BD, and 52 iSCC) in 160 patients were included. The HFUS features of AK, BD, and iSCC were analyzed. The obtained data were evaluated using univariate and forward multivariate logistic regression analyses. RESULTS: The most significant HFUS features in AK were regular surface (odds ratio [OR], 8.42) and irregular basal border (OR, 6.36). The most significant HFUS features in BD were crumpled surface (OR, 19.62) and layer involvement confined to the epidermis (OR, 3.96). The most significant HFUS features in iSCC were concave surface (OR, 27.06), stratum corneum (SC) detachment (OR, 14.41), irregular basal border (OR, 4.01), and convex surface (OR, 3.73). The characteristics of surface features, basal border, and layer involvement could be valuable HFUS clues in the discrimination of AK, BD, and iSCC. CONCLUSION: High-frequency ultrasound is valuable for the differentiation of AK, BD, and iSCC, which may allow dynamic and noninvasive monitoring in the spectrum of cSCC.

The therapeutic efficacy of 177Lu-DOTATATE/DOTATOC in advanced neuroendocrine tumors: A meta-analysis.

BACKGROUND: Somatostatin analog therapies showed great potential for patients suffering advanced neuroendocrine tumors (NETs). This study was aimed to evaluate the therapeutic efficacy of Lu-DOTATATE/DOTATOC (Lu-octreotate/octreotide) peptide receptor radionuclide therapy (PRRT) in advanced or inoperable NETs patients. METHODS: Pubmed, Web of Science, Embase and Cochrane Library were searched from 1950 to April 2019. Eligible studies should include randomized or nonrandomized controlled trials (RCTs)-based investigations of Lu-octreotate/octreotide PRRT for NETs. All these studies were assessed with Response Evaluation Criteria in Solid Tumors (RECIST), RECIST 1.1, Southwest Oncology Group (SWOG) criteria or World Health Organization (WHO) criteria. Disease response rates (DRRs) and disease control rates (DCRs) were calculated according to each response criteria group. DRRs were defined as the percentages of patients with complete response (CR) + partial response (PR), while DCRs represented the percentages of patients with CR+ PR+ stable disease (SD). The pooled proportions were calculated with either a fixed-effects model or a random-effects model depending on the test for heterogeneity. RESULTS: A total of 22 studies (1758 patients) were included in this meta-analysis: 8 studies with 478 patients met RECIST criteria, 10 studies with 1127 patients met RECIST 1.1 criteria, 5 studies with 459 patients met SWOG criteria, and 1 study with 40 patients met WHO criteria, and among these articles 1 study met both RECIST and RECIST 1.1 criteria and 1 met both RECIST 1.1 and SWOG criteria. The pooled DRRs were 33.0% (95% CI: 25.0%-42.0%, I = 65%), 35.0% (95% CI: 26.0%-45.0%, I = 91%) and 25.0% (95% CI: 14.0%-36.0%, I = 84%) according to RECIST, RECIST 1.1 and SWOG criteria, respectively. The pooled DCRs were 79.0% (95% CI: 75.0%-83.0%, I = 97%), 83.0% (95% CI: 78.0%-88.0%, I = 0) and 82.0% (95% CI: 75.0%-89.0%, I = 91%), respectively. CONCLUSION: In advanced NETs patients, DRRs and DCRs were significantly elevated after initial treatment with Lu-DOTATATE PRRT, which shows that this treatment would be beneficial and promising for advanced or inoperable NETs patients.