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 investigate the effect and predictive value of systemic inflammatory markers on pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) for locally advanced breast cancer (LABC). MethodsThe clinicopathologic data of female patients with LABC who received NACT and radical surgical resection in the Department of Breast Surgery, Affiliated Hospital of Southwest Medical University from February 2019 to February 2022 were retrospectively analyzed. The factors affecting pCR after NACT were analyzed by the multivariate logistic regression and the prediction model was established. The efficiency of the prediction model was evaluated by receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). ResultsA total of 98 patients were gathered, of which 29 obtained pCR, with a pCR rate of 29.6%. The multivariate analysis of binary logistic regression showed that the patients with non-menopausal status, negative estrogen receptor (ER), chemotherapy+targeted therapy, and systemic immune-inflammation index (SII) <532.70 (optimal critical value) were more likely to obtain pCR after NACT (P<0.05). The prediction model was established according to logistic regression analysis: Logit (P)=0.697–2.974×(menopausal status)–1.932×(ER status)+3.277×(chemotherapy regimen)–2.652×(SII). The AUC (95%CI) of the prediction model was 0.914 (0.840, 0.961), P<0.001. ConclusionsIt is not found that other inflammatory indicators such as neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio are associated with pCR after NACT. But SII is an important predictor of pCR after NACT for LABC and has a good predictive efficiency.
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.
ObjectiveTo observe the accuracy of magnetic resonance imaging (MRI) for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer, and to analyze the cause of the prediction error.MethodsData from 157 breast cancer patients who underwent NAC before surgery in Mianyang Central Hospital from January 2017 to January 2019 were analyzed. MRI parameters before and after NAC and pCR conditions were collected to analyze the parameters that produced false positives and false negatives.ResultsOf the 157 patients, 37 (23.6%) achieved pCR after NAC, and 33 (21.0%) achieved radiation complete remission (rCR) after NAC. The accuracy of MRI prediction was 70.7% (111/157), the sensitivity was 82.5% (99/120), and the specificity was 32.4% (12/37). A total of 25 cases did not achieve rCR, but postoperative evaluation achieved pCR (false positive), 21 cases achieved rCR, but postoperative evaluation did not achieve pCR (false negative). Diameter of tumor, peritumoral oedema, and background parenchymal enhancement were associated with MRI false positive prediction (P<0.05); gland density and tumor rim enhancement were associated with MRI false negative prediction (P<0.05).ConclusionMRI can be used as an important method to predict pCR after NAC in breast cancer patients, and its accuracy may be related to diameter of tumor, peritumoral oedema, background parenchymal enhancement, gland density, and tumor rim enhancement.
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.
ObjectiveTo analyze the association between nutritional and immune-related laboratory indices and pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients and focused on constructing a combination of laboratory indices to serve as a clinical predictor of pCR after NAC in breast cancer. MethodsRetrospectively collected the pre-NAC laboratory indices [albumin (ALB), total cholesterol, triglyceride, high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol, apolipoprotein A- Ⅰ, apolipoprotein B, white blood cell, neutrophil, lymphocyte, monocyte (MON), and platelet ] and clinicopathologic data of 310 patients with invasive breast cancer who had received NAC in the Department of Breast Surgery, Affiliated Hospital of Southwest Medical University, from September 1, 2020 to October 31, 2022. Logistic regression analysis was conducted to determine the correlation between laboratory indices and post-NAC pCR. The combinations of laboratory indices were constructed by simple mathematical operation. The area under the receiver operating characteristic curve (AUC) was used to evaluate the efficacy of different combinations of laboratory indices in predicting pCR and to determine the optimal combination of liboratory indices. Multivariate logistic regression analysis was used to analysis the relevance between clinicopathologic features and post-NAC pCR in breast cancer patients and to determine the independent predictor of post-NAC pCR. ResultsAmong the 310 patients, 49.4% (153/310) of them achieved pCR after NAC. Logistic regression analysis revealed that ALB (Z=5.203, P<0.001) and HDL-C (Z=2.129, P=0.033) were positively correlated with post-NAC pCR, while MON (Z=–4.883, P<0.001) was negatively correlated with post-NAC pCR. The AUC analysis of 6 different combinations of laboratory indices showed that the ALB/MON combination (the optimal combination of liboratory indices) had the highest predictive performance (median AUC=0.708) and was determined to be the neoadjuvant therapy predictive index (NTPI). Multivariate logistic regression analysis showed that estrogen receptor (Z=–3.273, P=0.001), human epidermal growth factor 2 (Z=7.041, P<0.001), Ki-67 (Z=2.457, P=0.014), and NTPI (Z=4.661, P<0.001) were the independent predictors for post-NAC pCR. ConclusionNTPI could serve as a predictive index for post-NAC pCR in patients with breast cancer.
Objective To explore the accuracy of contrast-enhanced magnetic resonance imaging (MRI) in predicting pathological complete remission (pCR) in breast cancer patients after neoadjuvant therapy (NAC). Methods The clinicopathological data of 245 patients with invasive breast cancer who had completed the surgical resection after NAC in the Affiliated Hospital of Southwest Medical University from March 2020 to April 2022 were collected retrospectively. According to the results of hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) detected by immunohistochemistry, all patients were divided into four subgroups: HR+/HER2–, HR+/HER2+, HR–/HER2+ and HR–/HER2–. The value of MRI in evaluating the efficacy of NAC was analyzed by comparing the postoperative pathological results as the gold standard with the residual tumor size assessed by preoperative MRI. Meanwhile, the sensitivity, specificity and positive predictive value (PPV) of pCR predicted by the evaluation results of enhanced MRI were analyzed, and further analyzed its predictive value for pCR of different subtypes of breast cancer. Results There were 88 cases (35.9%) achieved radiological complete response (rCR) and 106 cases (43.3%) achieved pCR in 245 patients. Enhanced MRI in assessing the size of residual tumors overestimated and underestimated 12.7% (31/245) and 9.8% (24/245) of patients, respectively. When setting rCR as the MRI assessment index the specificity, sensitivity and PPV were 84.2% (117/139), 62.3% (66/106) and 75.0% (66/88), respectively. When setting near-rCR as the MRI assessment index the specificity, sensitivity and PPV were 70.5% (98/139), 81.1% (86/106), and 67.7% (86/127), respectively. The positive predictive value of both MRI-rCR and MRI-near-rCR in evaluating pCR of each subtype subgroup of breast cancer was the highest in the HR–/HER2+ subgroup (91.7% and 83.3%, respectively). In each subgroup, compared with rCR, the specificity of near-rCR to predict pCR decreased to different degrees, while the sensitivity increased to different degrees. Conclusions Breast contrast-enhanced MRI can more accurately evaluate the efficacy of localized breast lesions after NAC, and can also more accurately predict the breast pCR after NAC. The HR–/HER2+ subgroup may be a potentially predictable population with pCR exemption from breast surgery. However, the accuracy of the evaluation of pCR by breast enhancement MRI in HR+/HER2– subgroup is low.
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.
Organ preservation after neoadjuvant therapy for esophageal cancer has gained significant attention. While the CROSS trial established neoadjuvant chemoradiotherapy (nCRT) followed by surgery as standard care, approximately 30% of patients achieve pathological complete response (pCR), prompting exploration of active surveillance (AS). The landmark SANO phase Ⅲ trial (2025) demonstrated non-inferior 2-year overall survival (74% AS vs. 71% surgery), with 31% of patients avoiding surgery. Multimodal assessment (endoscopic deep biopsy+EUS+PET-CT) reduced residual disease misdiagnosis to 10%. The Asian-led NEEDS trial is evaluating definitive chemoradiotherapy with salvage surgery. Although immunotherapy boosts pCR rates to 40%-55%, challenges persist, including 8%-12% false-negative cCR assessments, limited long-term data, and East-West histological disparities. The 2024 NCCN guidelines conditionally recommend AS (Category 2B, prioritized for squamous cell carcinoma), emphasizing centralized implementation. Future directions involve ctDNA and radiomics for risk stratification to advance precision organ-preserving strategies.
