ObjectiveTo analyze the factors influencing axillary pathological complete response (pCR) after neoadjuvant therapy (NAT) and to provide the possibility of exempting axillary surgery for patients with better pathological efficacy of primary breast lesions after NAT. MethodsAccording to the inclusion and exclusion criteria, the patients with breast cancer admitted to the Department of Breast Surgery, Affiliated Hospital of Southwest Medical University from January 1, 2020 to June 30, 2022 were retrospectively analyzed. All patients were diagnosed with ipsilateral axillary lymph node metastasis of breast cancer and the NAT cycle was completed according to standards. All patients underwent axillary lymph node dissection (ALND) after NAT. The therapeutic effect of primary breast lesions was evaluated by Miller-Payne (MP) grading system. The axillary pCR was judged according to whether there was residual positive axillary lymph nodes after ALND. The unvariate and multivariate logistic regressions were used to analyze the risk factors affecting the axillary pCR. At the same time, the possibility of exempting axillary surgery after NAT in the MP grade 5 or in whom without ductal carcinoma in situ (DCIS) was evaluated. The ALND was considered to exempt when the negative predictive value was 90% or more and false negative <10% or almost same. ResultsA total of 111 eligible patients with breast cancer were gathered in the study, 64 of whom with axillary pCR. There were 43 patients of MP grade 5 without DCIS after NAT, 41 of whom were axillary pCR. The univariate analysis results showed that the estrogen receptor and progesterone receptor statuses, molecular type, NAT regimen, and MP grade were associated with the axillary pCR after NAT, then the logistic regression multivariate analysis results showed that the MP grade ≤3 and MP grade 4 decreased the probability of axillary pCR as compared with the MP grade 5 [OR=0.105, 95%CI (0.028, 0.391), P=0.001; OR=0.045, 95%CI (0.012, 0.172), P<0.001]. There were 51 patients of MP grade 5 after NAT, 46 of whom were axillary pCR. The negative predictive value and the false negative rate of MP grade 5 on predicting the postoperative residual axillary lymph nodes were 90.2% [95%CI (81.7%, 98.6%)] and 10.6% [95%CI (1.5%, 19.8%)], respectively, which of MP grade 5 without DCIS were 95.3% [95%CI (88.8%, 101.9%)] and 4.3% [95%CI (–1.7%, 10.2%)] , respectively. ConclusionsThe probability of axillary pCR for the patient with higher MP grade of breast primary after NAT is higher. It is probable of exempting axillary surgery when MP grade is 5 after NAT.
ObjectiveTo summarize the current research progress in the prediction of the efficacy of neoadjuvant therapy of breast cancer based on the application of artificial intelligence (AI) and radiomics. MethodThe researches on the application of AI and radiomics in neoadjuvant therapy of breast cancer in recent 5 years at home and abroad were searched in CNKI, Google Scholar, Wanfang database and PubMed database, and the related research progress was reviewed. ResultsAI had developed rapidly in the field of medical imaging, and molybdenum target, ultrasound and magnetic resonance imaging combined with AI had been deepened and expanded in different degrees in the application research of breast cancer diagnosis and treatment. In the research of molybdenum target combined with AI, the high sensitivity of molybdenum target to microcalcification was mostly used to improve the accuracy of early detection and diagnosis of breast cancer, so as to achieve the clinical purpose of early detection and diagnosis. However, in terms of prediction of neoadjuvant efficacy research of breast cancer, ultrasound and magnetic resonance imaging combined with AI were more prevalent, and their popularity remained unabated. ConclusionIn the monitoring of neoadjuvant therapy for breast cancer, the use of properly designed AI and radiomics models can give full play to its role in the predicting the curative effect of neoadjuvant therapy, and help to guide doctors in clinical diagnosis and treatment and evaluate the prognosis of breast cancer patients.
With the rising incidence of breast cancer among women, neoadjuvant chemotherapy (NAC) is becoming increasingly crucial as a preoperative treatment modality, enabling tumor downstaging and volume reduction. However, its efficacy varies significantly among patients, underscoring the importance of predicting pathological complete response (pCR) following NAC. Early research relied on statistical methods to integrate clinical data for predicting treatment outcomes. With the advent of artificial intelligence (AI), traditional machine learning approaches were subsequently employed for efficacy prediction. Deep learning emerged to dominate this field, and demonstrated the capability to automatically extract imaging features and integrate multimodal data for pCR prediction. This review comprehensively examined the applications and limitations of these three methodologies in predicting breast cancer pCR. Future efforts must prioritize the development of superior predictive models to achieve precise predictions, integrate them into clinical workflows, enhance patient care, and ultimately improve therapeutic outcomes and quality of life.
Objective To investigate the clinicopathological characteristics of HER2 protein expression in different degrees in human epidermal growth factor receptor 2 (HER2) negative breast cancer and the factors related to the efficacy of neoadjuvant chemotherapy in breast cancer with low HER2 expression. Methods The clinicopathological data of 161 patients with HER2-negative breast cancer who received neoadjuvant chemotherapy in the Department of Breast Surgery, Affiliated Hospital of Southwest Medical University from March 2019 to March 2022 were retrospectively collected. The difference of clinical and pathological characteristics of patients with different levels of HER2 protein expression were analyzed, and the factors influencing the pathological complete remission (pCR) rate of breast cancer patients with low HER2 expression after neoadjuvant chemotherapy with unconditional logistic regression model were analyzed. Results Among 161 HER2 negative breast cancer patients, 108 cases were low HER2 expression, accounting for 67.1%. Compared with those with zero expression of HER2 [immunohistochemistry (IHC) 0], the patients with low HER2 expression had higher axillary lymph node metastasis rate (P=0.048), lower histological grade (P=0.006), and higher proportion of positive hormone receptor expression (P<0.001). There was no significant difference in pCR rate among the HER2 IHC 0, IHC 1+ and IHC 2+ / in situ hybridization (ISH)– (P=0.099) , and the pCR rate of low expression of HER2 was lower than that of zero expression of HER2 in the general population and Luminal subgroup, and the difference was statistically significant (P<0.05). There was no significant difference in triple negative breast cancer subgroup (P=0.814). The logistic regression analysis showed that age, histological grade and estrogen receptor expression status were independent influencing factors for pCR rate after neoadjuvant chemotherapy with low HER2 expression (P<0.05). Conclusions Different degrees of HER2 protein expressions in patients with HER2-negative breast cancer have unique clinicopathological characteristics. The pCR rate of neoadjuvant chemotherapy in patients with low HER2-expression breast cancer is lower than that in patients with zero HER2-expression breast cancer. Age, histological grade and estrogen receptor expression status are independent factors influencing the pCR rate of neoadjuvant chemotherapy in patients with low HER2-expression breast cancer.
ObjectiveTo explore the value of a decision tree (DT) model based on CT for predicting pathological complete response (pCR) after neoadjuvant chemotherapy therapy (NACT) in patients with locally advanced rectal cancer (LARC).MethodsThe clinical data and DICOM images of CT examination of 244 patients who underwent radical surgery after the NACT from October 2016 to March 2019 in the Database from Colorectal Cancer (DACCA) in the West China Hospital were retrospectively analyzed. The ITK-SNAP software was used to select the largest level of tumor and sketch the region of interest. By using a random allocation software, 200 patients were allocated into the training set and 44 patients were allocated into the test set. The MATLAB software was used to read the CT images in DICOM format and extract and select radiomics features. Then these reduced-dimensions features were used to construct the prediction model. Finally, the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), sensitivity, and specificity values were used to evaluate the prediction model.ResultsAccording to the postoperative pathological tumor regression grade (TRG) classification, there were 28 cases in the pCR group (TRG0) and 216 cases in the non-pCR group (TRG1–TRG3). The outcomes of patients with LARC after NACT were highly correlated with 13 radiomics features based on CT (6 grayscale features: mean, variance, deviation, skewness, kurtosis, energy; 3 texture features: contrast, correlation, homogeneity; 4 shape features: perimeter, diameter, area, shape). The AUC value of DT model based on CT was 0.772 [95% CI (0.656, 0.888)] for predicting pCR after the NACT in the patients with LARC. The accuracy of prediction was higher for the non-PCR patients (97.2%), but lower for the pCR patients (57.1%).ConclusionsIn this preliminary study, the DT model based on CT shows a lower prediction efficiency in judging pCR patient with LARC before operation as compared with homogeneity researches, so a more accurate prediction model of pCR patient will be optimized through advancing algorithm, expanding data set, and digging up more radiomics features.
Objective To systematically evaluate the efficacy and safety of dose-dense neoadjuvant chemotherapy (ddNACT) and conventional neoadjuvant chemotherapy (cNACT) for locally advanced breast cancer (LABC). Methods PubMed, Embase, Web of Science, CNKI, Wanfang Data, and VIP databases were searched for randomized controlled trials (RCT) comparing ddNACT regimen with cNACT regimen for breast cancer. The time limit for retrieval was from establishment to March 1st, 2021. Two reviewers independently screened literatures, extracted data and assessed risk bias of included studies; then, meta-analysis was performed by using Stata 15.0 software. Results A total of 13 RCTs were included, including 3 258 patients, of which 1 625 patients received ddNACT and 1 633 patients received cNACT. The results of meta-analysis showed that the ddNACT regimen could improve the pathological complete response rate (pCR, P<0.001), objective response rate (ORR, P<0.001), and disease free survival (DFS, P=0.037) as compared with the cNACT regimen, there was no significant difference in the overall survival (OS) between the two groups (P=0.098). The incidences of grade 3 or 4 oral stomatitis (P=0.005) and neurotoxicity (P<0.001) were higher and the incidence of grade 3 or 4 neutropenia was lower (P=0.025) in the patients with ddNACT regimen, there were no significant differences in grade 3 or 4 thrombocytopenia (P=0.152), grade 3 or 4 anemia (P=0.123), chemotherapy completion rate (P=0.161) and breast conservative surgery rate (P=0.186) between the two groups. Patients with hormone receptor (HR) negative (HR–) were more likely to get pCR after neoadjuvant chemotherapy (P<0.001). ConclusionsCurrent evidence shows that the use of anthracycline/taxane-based ddNACT regimen in LABC patients can improve the pCR, ORR, and DFS as compared with cNACT regimen. The pCR after neoadjuvant chemotherapy in the patients with HR– is higher than that with HR+. Prophylactic use of granulocyte-colony stimulating factor could significantly reduce the incidence of neutropenia, and most patients are tolerant to ddNACT regimen, 2 regimens have similar chemotherapy completion rates.
Objective To observe the short-term efficacy and safety of neoadjuvant sintilimab combined with chemotherapy in the treatment of patients with locally advanced resectable esophageal squamous cell carcinoma (ESCC). MethodsClinical data were collected from patients with locally advanced resectable ESCC who received neoadjuvant immunotherapy combined with chemotherapy followed by surgical treatment at the Department of Thoracic Surgery of Jining First People's Hospital from April 2020 to April 2022. The endpoints included major pathological response (MPR), pathological complete response (pCR), R0 resection rate, safety, and postoperative survival. Results A total of 43 patients with ESCC who received at least one cycle of neoadjuvant immunotherapy before surgery were included. Among them, there were 31 males and 12 females, aged from 46 to 77 years, with a median age of 65 years. All patients successfully completed the surgery without any surgical delays. The pCR rate was 14.0% (6/43), the MPR rate was 58.1% (25/43), and the R0 resection rate was 97.7% (42/43). Patients exhibited reliable safety during neoadjuvant therapy and postoperatively. The 2-year overall survival and disease-free survival rates were 90.7% and 81.4%, respectively. Kaplan-Meier survival analysis and log-rank test revealed lower recurrence rates and better survival in the MPR group compared to the non-MPR group. Conclusion The combination of neoadjuvant sintilimab and chemotherapy in the treatment of patients with locally advanced resectable ESCC has demonstrated significant clinical efficacy, while also being safe and reliable.
ObjectiveTo investigate the factors influencing pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) in patients with luminal breast cancer (LBC), and to construct and validate a nomogram-based predictive model. MethodsPatients with LBC who received NACT at the Affiliated Hospital of Southwest Medical University between January 2021 and February 2025 were retrospectively enrolled. Patients were randomly divided into training cohort (n=205) and validation cohort (n=87) by a ratio of 7∶3. Multivariate logistic regression analyses was performed in the training cohort, and a nomogram was developed based on the multivariate results. Model discrimination was evaluated using receiver operating characteristic (ROC) curves, calibration was assessed using calibration plots, and clinical utility was examined using decision curve analysis (DCA) in both cohorts. ResultsMultivariate logistic regression analysis in the training cohort showed that clinical tumor stage 4 [OR=0.018, 95%CI (0.001, 0.312), P=0.006], estrogen receptor expression>37.5% [OR=0.275, 95%CI (0.095, 0.798), P=0.018], and Ki-67 index>47.5% [OR=4.134, 95%CI (1.480, 11.544), P=0.007] were independent factors associated with pCR after NACT in LBC patients. A nomogram was constructed accordingly. The area under the ROC curve of the predictive model was 0.834 in the training cohort and 0.785 in the validation cohort. Calibration curves and Hosmer-Lemeshow tests demonstrated good predictive performance of the model in both cohorts (χ2=1.610, P=0.807; χ2=1.859, P=0.762). DCA indicated that the nomogram provided the greatest net benefit when the threshold probability ranged from 0% to 50% in both cohorts. ConclusionsClinical tumor stage, estrogen receptor expression level, and Ki-67 index were independent predictors of pCR after NACT in LBC patients. The nomogram constructed based on these factors showed good predictive performance in both the training and validation cohorts.
Objective To summarize the progress of biological indexes which could predict the efficiency of neoadjuvant chemotherapy for breast cancer. Methods Various related researches were collected to make a review. Results Many indexes linked to the efficiency of neoadjuvant chemotherapy for breast cancer according to several studies. According to many studies, indexes such as human epidermal growth factor receptor-2 (HER-2) gene, estrogen receptor (ER), progesterone receptor (PR), Ki-67, P53 gene, neutrophil to lymphocyte ratio (NLR), platelet level, and mean platelet volume (MPV) may have association with the outcome of neoadjuvant chemotherapy in treatment of breast cancer, and these factors maybe individual biomarkers to predict the efficiency of the treatment, but no coincident conclusion has been reached for these indexes. Conclusion The value of these indexes that predict the efficiency of neoadjuvant chemotherapy is not sure, further study need to be done to solve this topic.
Pancoast tumor, a special subtype of non-small cell lung cancer originating from the apex of the upper lobe, is characterized by its complex clinical manifestations and high treatment difficulty due to its unique anatomical location, often leading to a relatively poor prognosis. Currently, guidelines recommend neoadjuvant concurrent chemoradiotherapy followed by surgery as the standard treatment strategy, which has significantly improved overall patient survival compared to previous approaches. However, this regimen has limitations, including significant toxicity, increased surgical complexity, and a lack of individualized treatment options. In recent years, new strategies such as neoadjuvant targeted therapy and immunechemotherapy combinations have shown higher pathological response rates and manageable safety profiles in clinical studies, offering new directions for treating Pancoast tumors. This case report describes a 56-year-old female diagnosed with stage ⅢC Pancoast tumor harboring co-mutations in EGFR and ERBB2 and high PD-L1 expression. Through dynamic biopsy-guided precise targeted therapy, a neoadjuvant strategy incorporating immunotherapy and chemotherapy, and successful surgical intervention, pathological complete response was achieved. This case highlights the critical value of a multidisciplinary team approach and precision medicine in the management of Pancoast tumor.