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BACKGROUND: Not only should resistance to neoadjuvant chemotherapy (NAC) be considered in patients with breast cancer but also the possibility of achieving a pathologic complete response (PCR) after NAC. Our study aims to develop 2 multimodal ultrasound deep learning (DL) models to noninvasively predict resistance and PCR to NAC before treatment. METHODS: From January 2017 to July 2022, a total of 170 patients with breast cancer were prospectively enrolled. All patients underwent multimodal ultrasound examination (grayscale 2D ultrasound and ultrasound elastography) before NAC. We combined clinicopathological information to develop 2 DL models, DL_Clinical_resistance and DL_Clinical_PCR, for predicting resistance and PCR to NAC, respectively. In addition, these 2 models were combined to stratify the prediction of response to NAC. RESULTS: In the test cohort, DL_Clinical_resistance had an AUC of 0.911 (95%CI, 0.814-0.979) with a sensitivity of 0.905 (95%CI, 0.765-1.000) and an NPV of 0.882 (95%CI, 0.708-1.000). Meanwhile, DL_Clinical_PCR achieved an AUC of 0.880 (95%CI, 0.751-0.973) and sensitivity and NPV of 0.875 (95%CI, 0.688-1.000) and 0.895 (95%CI, 0.739-1.000), respectively. By combining DL_Clinical_resistance and DL_Clinical_PCR, 37.1% of patients with resistance and 25.7% of patients with PCR were successfully identified by the combined model, suggesting that these patients could benefit by an early change of treatment strategy or by implementing an organ preservation strategy after NAC. CONCLUSIONS: The proposed DL_Clinical_resistance and DL_Clinical_PCR models and combined strategy have the potential to predict resistance and PCR to NAC before treatment and allow stratified prediction of NAC response.
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Neoplasias da Mama , Aprendizado Profundo , Humanos , Feminino , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/patologia , Terapia Neoadjuvante , Estudos RetrospectivosRESUMO
BACKGROUND: Endosonographers are highly dependent on the diagnosis of pancreatic ductal adenocarcinoma (PDAC). The objectives of this study were to develop a deep-learning radiomics (DLR) model based on endoscopic ultrasonography (EUS) images for identifying PDAC and to explore its true clinical benefit. METHODS: A retrospective data set of EUS images that included PDAC and benign lesions was used as a training cohort (N = 368 patients) to develop the DLR model, and a prospective data set was used as a test cohort (N = 123 patients) to validate the effectiveness of the DLR model. In addition, seven endosonographers performed two rounds of reader studies on the test cohort with or without DLR assistance to further assess the clinical applicability and true benefits of the DLR model. RESULTS: In the prospective test cohort, DLR exhibited an area under the receiver operating characteristic curves of 0.936 (95% confidence interval [CI], 0.889-0.976) with a sensitivity of 0.831 (95% CI, 0.746-0.913) and 0.904 (95% CI, 0.820-0.980), respectively. With DLR assistance, the overall diagnostic performance of the seven endosonographers improved: one endosonographer achieved a significant expansion of specificity (p = .035,) and another achieved a significant increase in sensitivity (p = .038). In the junior endosonographer group, the diagnostic performance with the help of the DLR was higher than or comparable to that of the senior endosonographer group without DLR assistance. CONCLUSIONS: A prospective test cohort validated that the DLR model based on EUS images effectively identified PDAC. With the assistance of this model, the gap between endosonographers at different levels of experience narrowed, and the accuracy of endosonographers expanded.
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Carcinoma Ductal Pancreático , Aprendizado Profundo , Neoplasias Pancreáticas , Humanos , Endossonografia/métodos , Estudos Retrospectivos , Estudos Prospectivos , Neoplasias Pancreáticas/patologia , Carcinoma Ductal Pancreático/diagnóstico por imagem , Carcinoma Ductal Pancreático/patologia , Neoplasias PancreáticasRESUMO
BACKGROUND: Neoadjuvant chemotherapy (NAC) can downstage tumors and axillary lymph nodes in breast cancer (BC) patients. However, tumors and axillary response to NAC are not parallel and vary among patients. This study aims to explore the feasibility of deep learning radiomics nomogram (DLRN) for independently predicting the status of tumors and lymph node metastasis (LNM) after NAC. METHODS: In total, 484 BC patients who completed NAC from two hospitals (H1: 297 patients in the training cohort and 99 patients in the validation cohort; H2: 88 patients in the test cohort) were retrospectively enrolled. The authors developed two deep learning radiomics (DLR) models for personalized prediction of the tumor pathologic complete response (PCR) to NAC (DLR-PCR) and the LNM status (DLR-LNM) after NAC based on pre-NAC and after-NAC ultrasonography images. Furthermore, they proposed two DLRNs (DLRN-PCR and DLRN-LNM) for two different tasks based on the clinical characteristics and DLR scores, which were generated from both DLR-PCR and DLR-LNM. RESULTS: In the validation and test cohorts, DLRN-PCR exhibited areas under the receiver operating characteristic curves (AUCs) of 0.903 and 0.896 with sensitivities of 91.2% and 75.0%, respectively. DLRN-LNM achieved AUCs of 0.853 and 0.863, specificities of 82.0% and 81.8%, and negative predictive values of 81.3% and 87.2% in the validation and test cohorts, respectively. The two DLRN models achieved satisfactory predictive performance based on different BC subtypes. CONCLUSIONS: The proposed DLRN models have the potential to accurately predict the tumor PCR and LNM status after NAC. PLAIN LANGUAGE SUMMARY: In this study, we proposed two deep learning radiomics nomogram models based on pre-neoadjuvant chemotherapy (NAC) and preoperative ultrasonography images for independently predicting the status of tumor and axillary lymph node (ALN) after NAC. A more comprehensive assessment of the patient's condition after NAC can be achieved by predicting the status of the tumor and ALN separately. Our model can potentially provide a noninvasive and personalized method to offer decision support for organ preservation and avoidance of excessive surgery.
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Neoplasias da Mama , Aprendizado Profundo , Humanos , Feminino , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/patologia , Estudos Retrospectivos , Terapia Neoadjuvante/métodos , Linfonodos/diagnóstico por imagem , Linfonodos/patologia , Ultrassonografia , Metástase Linfática/patologiaRESUMO
Neoadjuvant chemotherapy (NAC) is the primary method to reduce the burden of tumor and metastasis; in the treatment of breast cancer, it may provide additional opportunities for breast-conserving surgery. Preoperative assessment of pathological complete response (PCR) to NAC is important for developing individualized treatment approaches and predicting patient prognosis. Compared to magnetic resonance imaging (MRI) and mammography, ultrasonography (US) has the advantages of simplicity, flexibility, and real-time imaging. Moreover, it does not require radiation and can provide multi-time acquisition of the tumor during NAC treatment. Recently, deep learning radiomics models based on multi-time-point US images for the prediction of NAC effectiveness have been proposed. To further improve the prediction performance, we carefully designed four supporting modules for our proposed dual-input transformer (DiT): isolated tokens-to-token patch embedding module, shared position embedding, time embedding, and weighted average pooling feature representation modules. The design of each module considers the characteristics of the US images at multiple time points. We validated our model on our retrospective US dataset composed of 484 cases from two centers whose consistency is not sufficiently high. Patients were allocated to training (n = 297), validation (n = 99), and external test (n = 88) sets. The results show that our model can achieve better performance than the Siamese CNN and the standard tokens-to-token vision transformer without using multi-time-point images. The ablation study also proved the effectiveness of each module designed for DiT.
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Neoplasias da Mama , Feminino , Humanos , Neoplasias da Mama/patologia , Terapia Neoadjuvante/métodos , Estudos Retrospectivos , Resultado do Tratamento , Ultrassonografia Mamária/métodos , Imageamento por Ressonância Magnética/métodosRESUMO
Breast cancer is the most common cancer in women worldwide. Providing accurate and efficient diagnosis, risk stratification and timely adjustment of treatment strategies are essential steps in achieving precision medicine before, during and after treatment. Radiomics provides image information that cannot be recognized by the naked eye through deep mining of medical images. Several studies have shown that radiomics, as a second reader of medical images, can assist physicians not only in the detection and diagnosis of breast lesions but also in the assessment of risk stratification and prediction of treatment response. Recently, more and more studies have focused on the application of ultrasound radiomics in breast management. We summarized recent research advances in ultrasound radiomics for the diagnosis of benign and malignant breast lesions, prediction of molecular subtype, assessment of lymph node status, prediction of neoadjuvant chemotherapy response, and prediction of survival. In addition, we discuss the current challenges and future prospects of ultrasound radiomics.
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BACKGROUND: Accurate and non-invasive diagnosis of pancreatic ductal adenocarcinoma (PDAC) and chronic pancreatitis (CP) can avoid unnecessary puncture and surgery. This study aimed to develop a deep learning radiomics (DLR) model based on contrast-enhanced ultrasound (CEUS) images to assist radiologists in identifying PDAC and CP. METHODS: Patients with PDAC or CP were retrospectively enrolled from three hospitals. Detailed clinicopathological data were collected for each patient. Diagnoses were confirmed pathologically using biopsy or surgery in all patients. We developed an end-to-end DLR model for diagnosing PDAC and CP using CEUS images. To verify the clinical application value of the DLR model, two rounds of reader studies were performed. RESULTS: A total of 558 patients with pancreatic lesions were enrolled and were split into the training cohort (n=351), internal validation cohort (n=109), and external validation cohorts 1 (n=50) and 2 (n=48). The DLR model achieved an area under curve (AUC) of 0.986 (95% CI 0.975-0.994), 0.978 (95% CI 0.950-0.996), 0.967 (95% CI 0.917-1.000), and 0.953 (95% CI 0.877-1.000) in the training, internal validation, and external validation cohorts 1 and 2, respectively. The sensitivity and specificity of the DLR model were higher than or comparable to the diagnoses of the five radiologists in the three validation cohorts. With the aid of the DLR model, the diagnostic sensitivity of all radiologists was further improved at the expense of a small or no decrease in specificity in the three validation cohorts. CONCLUSIONS: The findings of this study suggest that our DLR model can be used as an effective tool to assist radiologists in the diagnosis of PDAC and CP.
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Carcinoma Ductal Pancreático , Aprendizado Profundo , Neoplasias Pancreáticas , Pancreatite Crônica , Carcinoma Ductal Pancreático/diagnóstico por imagem , Humanos , Neoplasias Pancreáticas/patologia , Pancreatite Crônica/diagnóstico por imagem , Estudos RetrospectivosRESUMO
BACKGROUND: The aim of this study was to investigate the diagnosis performance of new shear wave elastography (sound touch elastography, STE) in the prediction of neoadjuvant chemotherapy (NAC) response at an early stage in breast cancer patients and to determine the optimal measurement locations around the lesion in different ranges. METHODS: One hundred and eight patients were analyzed in this prospective study from November 2018 to December 2020. All patients completed NAC treatment and underwent STE examination at three time points [the day before NAC (t0); the day before the second course (t1); the day before third course (t2)]. The stiffness of the whole lesion (G), 1-mm shell (S1) and 2-mm shell (S2) around the lesion was expressed by STE parameters. The relative changes (∆stiffness) of STE parameters after the first and second course of NAC were calculated and shown as the variables [Δ(t1) and Δ(t2)]. The diagnostic accuracy of STE was evaluated by means of receiver operating characteristic curve analysis. RESULTS: The ∆stiffness (%) including ∆Gmean(t2), ∆S1mean(t2) and ∆S2mean(t2) all showed significant differences between pathological complete response (pCR) and non-pCR groups. ∆S2mean(t2) displayed the best predictive performance for pCR (AUC = 0.842) with an ideal ∆stiffness threshold value - 26%. CONCLUSIONS: Measuring the relative changes in the stiffness of surrounding tissue or entire lesion with STE holds promise for effectively predicting the response to NAC at its early stage for breast cancer patients and ∆stiffness of shell 2 mm after the second course of NAC may be a potential prediction parameter.
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Neoplasias da Mama , Técnicas de Imagem por Elasticidade , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/patologia , Feminino , Humanos , Terapia Neoadjuvante , Estudos Prospectivos , Curva ROCRESUMO
OBJECTIVES: Breast cancer (BC) is the most common cancer in women worldwide, and neoadjuvant chemotherapy (NAC) is considered the standard of treatment for most patients with BC. However, response rates to NAC vary among patients, which leads to delays in appropriate treatment and affects the prognosis for patients who ineffectively respond to NAC. This study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage. METHODS: In total, 168 patients with clinicopathologically confirmed BC were enrolled in this prospective study, from March 2016 to December 2020. All patients completed NAC treatment and underwent ultrasonography (US) at three time points (before NAC, after the second course, and after the fourth course). We developed two DLR models, DLR-2 and DLR-4, for predicting responses after the second and fourth courses of NAC. Furthermore, a novel deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response at different time points of NAC administration. RESULTS: In the validation cohort, DLR-2 achieved an AUC of 0.812 (95% CI: 0.770-0.851) with an NPV of 83.3% (95% CI: 76.5-89.6). DLR-4 achieved an AUC of 0.937 (95% CI: 0.913-0.955) with a specificity of 90.5% (95% CI: 86.3-94.2). Moreover, 19 of 21 non-response patients were successfully identified by DLRP, suggesting that they could benefit from treatment strategy adjustment at an early stage of NAC. CONCLUSIONS: The proposed DLRP strategy holds promise for effectively predicting NAC response at its early stage for BC patients. KEY POINTS: ⢠We proposed two novel deep learning radiomics (DLR) models to predict response to neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on US images at different NAC time points. ⢠Combining two DLR models, a deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response to NAC. ⢠The DLRP may provide BC patients and physicians with an effective and feasible tool to predict response to NAC at an early stage and to determine further personalized treatment options.
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Neoplasias da Mama , Aprendizado Profundo , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/tratamento farmacológico , Feminino , Humanos , Terapia Neoadjuvante , Estudos Prospectivos , Estudos Retrospectivos , UltrassonografiaRESUMO
PURPOSE: To explore the usefulness of liver stiffness measurements (LSMs) by sound touch elastography (STE) and sound touch quantification (STQ) in chronic hepatitis B (CHB) patients for staging fibrosis. METHODS: This prospective multicenter study recruited normal volunteers and CHB patients between May 2018 and October 2019. The volunteers underwent LSM by STE and supersonic shear imaging (SSI) or by STQ and acoustic radiation force impulse imaging (ARFI). CHB patients underwent liver biopsy and LSM by both STE/STQ. The areas under the receiver operating characteristic curves (AUCs) for staging fibrosis were calculated. RESULTS: Overall, 97 volunteers and 524 CHB patients were finally eligible for the study. The successful STE and STQ measurement rates were both 100â% in volunteers and 99.4â% in CHB patients. The intraclass correlation coefficients (ICCs) for the intra-observer stability of STE and STQ (0.94; 0.90) were similar to those of SSI and ARFI (0.95; 0.87), respectively. STE and STQ showed better accuracy than the aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 index (FIB-4) (AUC: 0.87 vs 0.86 vs 0.73 vs 0.77) in staging cirrhosis. However, both STE and STQ were not superior to APRI and FIB-4 in staging significant fibrosis (AUC: 0.76 vs 0.73 vs 0.70 vs 0.71, all P-values >â0.05). CONCLUSION: STE and STQ are convenient techniques with a reliable LSM value. They have a similar diagnostic performance and are superior to serum biomarkers in staging cirrhosis in CHB patients.
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Técnicas de Imagem por Elasticidade , Hepatite B Crônica , Aspartato Aminotransferases , Biópsia , Técnicas de Imagem por Elasticidade/métodos , Hepatite B Crônica/complicações , Hepatite B Crônica/diagnóstico por imagem , Hepatite B Crônica/patologia , Humanos , Fígado/diagnóstico por imagem , Fígado/patologia , Cirrose Hepática/diagnóstico por imagem , Cirrose Hepática/patologia , Estudos Prospectivos , Curva ROCRESUMO
PURPOSE: This study aimed to investigate the technical efficiency and therapeutic response of fusion imaging (considered as virtual navigation) between contrast-enhanced ultrasound (CEUS) and contrast-enhanced computed tomography/magnetic resonance imaging (CECT/CEMRI) for the guidance of radiofrequency ablation (RFA) in patients with residual hepatocellular carcinoma (HCC) after transcatheter arterial chemoembolization (TACE). METHODS: For this prospective study, 98 patients with residual HCC lesions after TACE treatment were enrolled between June 2017 and December 2020. All the lesions were invisible on conventional ultrasound scans. Percutaneous RFA was performed using either CEUS (CEUS group, 52 lesions) or virtual navigation (VN group, 46 lesions) guidance. The lesion display rate, disease-free survival rate, local recurrence rate, overall survival rate and complication incidence were calculated and compared. RESULTS: Fusion imaging had a significant impact on the RFA outcomes (hazard ratio, 2.629; 95% confidence interval, 1.256-5.505; p = .01). The median disease-free survival time of the VN group was significantly higher than that of the CEUS group (10.9 vs. 8.8 months; p = .007). The local recurrence rates after 3, 6 and 12 months in the VN group were significantly lower than those in the CEUS group (p = .014, .002 and .011). The minor complication rate was not significantly different between the two groups. CONCLUSIONS: CEUS-CECT/CEMRI fusion imaging for guiding RFA enables an efficient and useful therapy of inconspicuous HCC lesions after TACE. The novel solution prolongs the disease-free survival time and reduces the long-term local recurrence of residual lesions treated when using virtual-navigation (VN)-guided RFA.
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Carcinoma Hepatocelular , Ablação por Cateter , Quimioembolização Terapêutica , Neoplasias Hepáticas , Ablação por Radiofrequência , Carcinoma Hepatocelular/diagnóstico por imagem , Carcinoma Hepatocelular/cirurgia , Humanos , Neoplasias Hepáticas/diagnóstico por imagem , Neoplasias Hepáticas/cirurgia , Recidiva Local de Neoplasia/diagnóstico por imagem , Estudos Prospectivos , Estudos Retrospectivos , Resultado do TratamentoRESUMO
ABSTRACT: The ability of ultrasound elastography to diagnose focal liver lesions and determine their prognoses including hepatocellular carcinoma (HCC) is unclear. At present, radiofrequency ablation and liver resection are the most common treatments for HCC. However, the survival rate remains disappointing because of recurrences and postoperative liver failure, necessitating the development of noninvasive approaches. There is currently no systematic definition of an elastic technique for measuring liver stiffness to predict the recurrence of HCC after radiofrequency ablation and postoperative liver failure. In this review, recent advances in ultrasound elastography for the diagnosis and prognosis of focal liver lesions are discussed including HCC.
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Carcinoma Hepatocelular , Ablação por Cateter , Técnicas de Imagem por Elasticidade , Neoplasias Hepáticas , Carcinoma Hepatocelular/diagnóstico por imagem , Carcinoma Hepatocelular/cirurgia , Humanos , Neoplasias Hepáticas/diagnóstico por imagem , Neoplasias Hepáticas/cirurgia , Recidiva Local de Neoplasia , PrognósticoRESUMO
Medical imaging provides a comprehensive perspective and rich information for disease diagnosis. Combined with artificial intelligence technology, medical imaging can be further mined for detailed pathological information. Many studies have shown that the macroscopic imaging characteristics of tumors are closely related to microscopic gene, protein and molecular changes. In order to explore the function of artificial intelligence algorithms in in-depth analysis of medical imaging information, this paper reviews the articles published in recent years from three perspectives: medical imaging analysis method, clinical applications and the development of medical imaging in the direction of pathological molecular prediction. We believe that AI-aided medical imaging analysis will be extensively contributing to precise and efficient clinical decision.
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The aim of this study was to pre-operatively investigate the diagnostic performance of 2D shear wave elastography (2D-SWE) for staging liver fibrosis and inflammation in patients with hepatocellular carcinoma (HCC) who then undergo surgery and to determine the optimal locations for measurement. In total, 106 patients were enrolled in this prospective study from March 2017 to May 2018. Two-dimensional SWE was used to measure liver stiffness (LS) in each patient 0-1, 1-2 and 2-5 cm from the tumor border (groups 1, 2 and 3, respectively). Spearman's correlation was used to evaluate the relationships between LS and hepatic fibrosis and between LS and inflammation. Receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic accuracy of 2D-SWE. The technical success rate of SWE in tissue distant from the tumor (group 3) was significantly higher than that in peri-tumoral tissue (groups 1 and 2) (p < 0.001). Moreover, the area under the ROC for diagnosing cirrhosis (F4) and severe inflammation (A3) was higher for group 3 than for groups 1 and 2. Our results suggest that 2D-SWE is a helpful approach to assessment of hepatic fibrosis in HCC patients before hepatic resection. We found that to achieve a superior success rate and preferable diagnosis accuracy for patients with HCC, LS measurement should be performed 2-5 cm from the tumor margin.
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Carcinoma Hepatocelular/patologia , Técnicas de Imagem por Elasticidade/métodos , Cirrose Hepática/diagnóstico por imagem , Neoplasias Hepáticas/patologia , Cuidados Pré-Operatórios , Adulto , Idoso , Carcinoma Hepatocelular/sangue , Feminino , Hepatite/diagnóstico por imagem , Humanos , Cirrose Hepática/sangue , Masculino , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Estudos Prospectivos , Curva ROCRESUMO
BACKGROUND Ultrasonography-guided percutaneous drainage for pancreatic fluid collections is associated with a high recurrence rate and endoscopic ultrasonography (EUS)-guided drainage is a valuable approach. Our aim was to compare the efficacy and safety of percutaneous and EUS-guided drainage for the recurrent pancreatic fluid collections. MATERIAL AND METHODS A retrospective analysis of percutaneous-guided and EUS-guided procedures for pancreatic fluid collections drainages at a single tertiary care center between February 2017 and May 2018 was performed. Treatment success, adverse events, recurrence, need for surgery, length of hospital stays, and number of follow-up computed tomography (CT) scan were assessed. RESULTS A total of 119 pancreatic fluid collections treated with initial percutaneous drainage were included in this study and 35 patients had recurrent pancreatic fluid collections. Recurrent patients were classified based on drainage method: EUS-guided drainage (18 patients) and the second percutaneous drainage (17 patients). EUS-guided drainage revealed a shorter length of hospital stays (P<0.001), less re-intervention (P=0.047), fewer number of follow-up CT scans (P=0.006) compared with the initial percutaneous drainage. Furthermore, we also compared the clinical outcomes between the EUS-guided drainage and the second percutaneous drainage for the recurrent PFC after initially failed percutaneous drainage. EUS-guided drainage showed higher clinical success (P=0.027), shorter length of hospital stays (P<0.001), less re-intervention (P=0.012), fewer number of follow-up CT scan (P<0.001) and less recurrence P=0.027) compared to the second percutaneous drainage procedure. CONCLUSIONS EUS-guided drainage is an effective and appropriate method to treat the recurrent pancreatic fluid collections after initially failed percutaneous drainage procedure, with the advantage of higher clinical success, shorter length of hospital stays, less re-intervention, fewer number of follow-up CT scan and less recurrence compared to the percutaneous drainage.