A pathologic method for extracting good prognosis group in triple negative breast cancer after neoadjuvant chemotherapy

Digitally quantified area of residual tumor after neoadjuvant chemotherapy in HER2-positive breast cancer

BACKGROUND

The area of residual tumor (ART) is a quantitative method for assessing tumors after neoadjuvant chemotherapy (NAC). This study evaluated whether ART can identify a favorable prognosis group in patients with HER2-positive surgically resected breast cancer and residual tumors post-NAC.

METHODS

We retrospectively reviewed patients with HER2-positive who underwent surgery after NAC, including trastuzumab, from 2005 to 2022 at our institution. ART was assessed at the maximum cut surface of the residual primary tumor using digital pathology images. Receiver operating characteristic curve analysis determined ART-Low and ART-High cutoffs, excluding ART-0 (0 mm2) patients.

RESULTS

Of the 219 patients, 82 had ART greater than 0 mm2. The median follow-up was 90.2 months. The number of patients in the ART-0, ART-Low (0 < ART ≤ 4.0 mm2), and ART-High (> 4.0 mm2) groups were 137, 39, and 43, respectively. The ART-Low group showed significantly shorter event-free survival compared to the ART-0 group (HR 3.50, 95% CI 1.52-8.06), and the ART-High group also tended toward poorer prognosis (HR 2.31, 95% CI 0.89-5.97). However, there was no significant difference in prognosis between the ART-Low and ART-High groups.

CONCLUSIONS

The current study suggests that even minimal residual tumor cells in the primary site can significantly impact on prognosis in HER2-positive early breast cancer.

Area of Residual Tumor Measurement After Preoperative Chemotherapy as an Objective and Quantitative Method for Predicting the Prognosis of Gastric Cancer: A Single-Center Retrospective Study

BACKGROUND

Pathological regression grade after chemotherapy evaluated by surgically resected specimens is closely related with prognosis. Since usefulness of measuring the area of the residual tumor (ART) has been reported, this study aimed to evaluate the utility of ART in predicting the prognosis of patients with gastric cancer (GC) who received preoperative chemotherapy.

METHODS

This single-center retrospective study examined the relationship between ART and survival outcomes. We included 92 patients who underwent preoperative chemotherapy followed by radical surgery for GC. Digital images were used to measure the ART in the largest pathological slice of each patient's surgical tumor specimen. We simply subclassified the patients as either ART-0 (< 0.1 mm2 or carcinoma in situ) or non-ART-0 to compare the prognoses.

RESULTS

Significant differences were noted in overall survival and recurrence-free survival (RFS) between ART-0 (n = 19) and non-ART-0 (n = 73). The survival curves were similar to those of major pathological response (MPR) (n = 24) or non-MPR (n = 68), which are commonly used as surrogate endpoint presently. Multivariate analysis revealed ART and ypN independent prognostic factors for RFS. Survival curves stratified using ART and ypN to indicate risk grades (low-, moderate-, or high-) were not significantly different from those stratified using the other three existing pathological regression grade systems and ypN.

CONCLUSION

ART-based pathological assessment is a simple and useful method for predicting the prognosis in patients with GC who underwent radical surgery after chemotherapy.

Comparison of the effectiveness of neoadjuvant chemotherapy and adjuvant chemotherapy for improving prognosis in triple-negative breast cancer patients

OBJECTIVE

To compare the effectiveness of surgery combined with neoadjuvant chemotherapy and radiotherapy (SNCR) versus surgery combined with adjuvant chemotherapy and radiotherapy (SACR) in improving the prognosis of triple-negative breast cancer (TNBC) patients.

METHODS

Clinical data from 112 TNBC patients treated between January 2014 and February 2019 were retrospectively collected. Data included clinical characteristics and 5-year disease-free survival (DFS). Kaplan-Meier (K-M) survival curves were used to analyze the associations of various factors with DFS. Lasso-Cox regression was used to screen significant variables identified by K-M survival analysis. Multivariate Cox regression was used to determine independent prognostic factors affecting DFS.

RESULTS

K-M survival analysis showed that treatment regimen (P=0.012), TNM (tumor, node, metastasis) staging (P=0.049), N staging (P=0.015), P53 (P=0.015), KI-67 (P=0.002), neutrophil-to-lymphocyte ratio (NLR) (P<0.001), platelet-to-lymphocyte ratio (PLR) (P<0.001), and cancer antigen 153 (CA153) (P<0.001) were associated with DFS in TNBC patients. Lasso-Cox regression analysis identified treatment regimen, TNM stage, P53, KI-67, NLR, PLR, and CA153 as features related to DFS when λ=0.053741 (1se). Multivariate Cox regression analysis revealed that treatment regimen (P<0.001, 95% CI: 2.309-14.396, HR=5.765), P53 (P=0.010, 95% CI: 1.315-7.864, HR=3.216), and NLR (P=0.001, 95% CI: 2.098-14.553, HR=5.525) were independent prognostic factors affecting DFS. A nomogram model was constructed, and time-dependent receiver operating characteristic (ROC) curve analysis showed that the model's areas under the curve (AUC) for predicting 1-, 3-, and 5-year DFS were 0.928, 0.816, and 0.665, respectively.

CONCLUSION

The SNCR regimen significantly improves DFS in patients with stage IIb to IIIa TNBC compared to the traditional SACR regimen.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

BACKGROUND

Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response.

METHODS

H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.

RESULTS

The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.

CONCLUSION

Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

Background

Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment (TME) in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response.

Methods

H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) were separated through a stratified 8-fold cross validation strategy for the first step and leave one out cross validation strategy for the second step. A tile-level histology label prediction pipeline and four machine learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.

Results

The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.

Conclusion

Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

The area of residual tumor predicts esophageal squamous cell carcinoma prognosis following neoadjuvant chemotherapy

PURPOSE

To clarify the utility of the area of residual tumor for patients with esophageal squamous cell cancer treated with neoadjuvant chemotherapy.

METHODS

We enrolled 186 patients with esophageal squamous cell cancer who underwent surgical resection following neoadjuvant chemotherapy at our hospital. Using digital images, we measured the area of residual tumor at the maximum plane of the specimen and divided the patient into three groups as follows: 0 (area = 0 mm2), low (area = 0-40 mm2), and high (area ≥ 40 mm2). The clinicopathological factors and prognosis were compared among these groups.

RESULTS

The median area of the residual tumor was 15.0 mm2　(range 0-1,448.8 mm2). Compared with the 0 and low group, the high group was significantly associated with poorer recurrence-free survival (all P < .001) and overall survival (P < .001 [vs. 0] and P = .017 [vs low]). The area of residual tumor, ypN, tumor regression grade, and lymphovascular invasion were independent predictors of recurrence-free survival. By dividing the patients using a combination of the area of residual tumor and lymphovascular invasion, the high and/or lymphovascular invasion ( +) group displayed significantly poor recurrence-free survival than the 0 group and low/lymphovascular invasion ( -) group. However, there was no significant difference in the recurrence-free survival between the 0 group and low/lymphovascular invasion ( -) group.

CONCLUSION

The area of residual tumor is a promising histopathological prognostic factor for patients with esophageal squamous cell cancer treated with neoadjuvant chemotherapy. Moreover, it is a possible candidate histopathological factor for postoperative chemotherapy selection.