Novel tumor-infiltrating lymphocytes ultrasonography score based on ultrasonic tissue findings predicts tumor-infiltrating lymphocytes in breast cancer

Diagnostic performance of TILs-US score and LPBC in biopsy specimens for predicting pathological complete response in patients with breast cancer

BACKGROUND

Tumor-infiltrating lymphocytes-ultrasonography (TILs-US) score is used to predict lymphocyte-predominant breast cancer (LPBC) in surgical specimens. We aimed to compare diagnostic performance of TILs-US score for predicting pathological complete response (pCR) with that of LPBC in biopsy specimens.

METHODS

TILs ≥ 50% in biopsy specimens was defined as biopsy-LPBC, and TILs-US score ≥ 4 was categorized as TILs-US score-high. Basic nomogram for pCR was developed using stepwise logistic regression based on the smallest Akaike Information Criterion, and biopsy-LPBC and TILs-US score nomograms were developed by integrating biopsy-LPBC or TILs-US scores into a basic nomogram. The diagnostic performance of the nomograms for pCR was compared using area under the curve (AUC), categorical net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

RESULTS

This retrospective study evaluated 118 patients with breast cancer, including 33 (28.0%) with biopsy-LPBC, 52 (44.1%) with TILs-US score-high, with 34 (28.8%) achieving pCR. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and AUC for predicting pCR were 0.53, 0.82, 2.96, 0.57, and 0.68, respectively, for biopsy-LPBC, and 0.76, 0.69, 2.47, 0.34, and 0.73, respectively, for TILs-US score. The biopsy-LPBC nomogram showed significant improvements in categorical NRI (p = 0.023) and IDI (p = 0.007) but not in AUC (p = 0.25), compared with the basic nomogram. The TILs-US nomogram exhibited significant improvements in AUC (p = 0.039), categorical NRI (p = 0.010), and IDI (p < 0.001).

CONCLUSIONS

The TILs-US score may serve as a novel marker for prediction of pCR in patients with breast cancer. An external validation study is warranted to confirm our findings.

A nomogram to predict the pathological complete response in patients with breast cancer based on the TILs-US score

BACKGROUND

The tumor-infiltrating lymphocytes-ultrasonography score is a calculation system for predicting lymphocyte-predominant breast cancers in surgical specimens. A nomogram based on the tumor-infiltrating lymphocytes-ultrasonography score was developed to predict the pathological complete response in breast cancer treated with neoadjuvant chemotherapy.

METHODS

A retrospective evaluation was conducted on 118 patients with breast cancer treated with neoadjuvant chemotherapy at Hiroshima University Hospital. Tumor-infiltrating lymphocytes-ultrasonography scores ≥4 were classified as high. A nomogram was developed using a stepwise logistic regression model for pathological complete response (ypT0 ypN0), based on the smallest Akaike information criterion. The predictive ability and clinical usefulness of the nomogram were also evaluated.

RESULTS

Among 118 patients, 34 (28.8%) achieved a pathological complete response, and 52 (44.1%) exhibited high tumor-infiltrating lymphocytes-ultrasonography. In multivariate logistic regression analysis, high tumor-infiltrating lymphocytes-ultrasonography (odds ratio, 6.01; P < 0.001), clinical complete response (odds ratio, 4.83; P = 0.004) and hormone receptor (odds ratio, 3.48; P = 0.038) were independent predictors of pathological complete response. A nomogram based on tumor-infiltrating lymphocytes-ultrasonography score, clinical complete response, hormone receptor and clinical N status was developed. The nomogram showed an area under the curve of 0.831 and a bias-corrected area under the curve of 0.809. The calibration plot showed a good fit between the expected and actual pathological complete response values. Decision curve analysis also showed the clinical utility of the nomogram for predicting pathological complete responses.

CONCLUSIONS

A nomogram based on the tumor-infiltrating lymphocytes-ultrasonography score exhibited a favorable predictive ability for pathological complete response in patients with breast cancer, which can be useful in predicting the residual disease status after neoadjuvant chemotherapy.

Association of clinicopathologic and sonographic features with stromal tumor-infiltrating lymphocytes in triple-negative breast cancer

BACKGROUND

Increased level of stromal tumor-infiltrating lymphocytes (sTILs) are associated with therapeutic outcomes and prognosis in triple-negative breast cancer (TNBC). This study aimed to investigate the associations of clinicopathologic and sonographic features with sTILs level in TNBC.

METHODS

This study included invasive TNBC patients with postoperative evaluation of sTILs after surgical resection. Tumor shape, margin, orientation, echo pattern, posterior features, calcification, and vascularity were retrospectively evaluated. The patients were categorized into high-sTILs (≥ 20%) and low-sTILs (< 20%) level groups. Chi-square or Fisher's exact tests were used to assess the association of clinicopathologic and sonographic features with sTILs level.

RESULTS

The 171 patients (mean ± SD age, 54.7 ± 10.3 years [range, 22‒87 years]) included 58.5% (100/171) with low-sTILs level and 41.5% (71/171) with high-sTILs level. The TNBC tumors with high-sTILs level were more likely to be no special type invasive carcinoma (p = 0.008), higher histologic grade (p = 0.029), higher Ki-67 proliferation rate (all p < 0.05), and lower frequency of associated DCIS component (p = 0.026). In addition, the TNBC tumors with high-sTILs level were more likely to be an oval or round shape (p = 0.001), parallel orientation (p = 0.011), circumscribed or micro-lobulated margins (p < 0.001), complex cystic and solid echo patterns (p = 0.001), posterior enhancement (p = 0.002), and less likely to have a heterogeneous pattern (p = 0.001) and no posterior features (p = 0.002).

CONCLUSIONS

This preliminary study showed that preoperative sonographic characteristics could be helpful in distinguishing high-sTILs from low-sTILs in TNBC patients.

Prediction value study of breast cancer tumor infiltrating lymphocyte levels based on ultrasound imaging radiomics

Objective

Construct models based on grayscale ultrasound and radiomics and compare the efficacy of different models in preoperatively predicting the level of tumor-infiltrating lymphocytes in breast cancer.

Materials and methods

This study retrospectively collected clinical data and preoperative ultrasound images from 185 breast cancer patients confirmed by surgical pathology. Patients were randomly divided into a training set (n=111) and a testing set (n=74) using a 6:4 ratio. Based on a 10% threshold for tumor-infiltrating lymphocytes (TIL) levels, patients were classified into low-level and high-level groups. Radiomic features were extracted and selected using the training set. The evaluation included assessing the relationship between TIL levels and both radiomic features and grayscale ultrasound features. Subsequently, grayscale ultrasound models, radiomic models, and nomograms combining radiomics score (Rad-score) and grayscale ultrasound features were established. The predictive performance of different models was evaluated through receiver operating characteristic (ROC) analysis. Calibration curves assessed the fit of the nomograms, and decision curve analysis (DCA) evaluated the clinical effectiveness of the models.

Results

Univariate analyses and multivariate logistic regression analyses revealed that indistinct margin (P<0.001, Odds Ratio [OR]=0.214, 95% Confidence Interval [CI]: 0.103-1.026), posterior acoustic enhancement (P=0.027, OR=2.585, 95% CI: 1.116-5.987), and ipsilateral axillary lymph node enlargement (P=0.001, OR=4.214, 95% CI: 1.798-9.875) were independent predictive factors for high levels of TIL in breast cancer. In comparison to grayscale ultrasound model (Training set: Area under curve [AUC] 0.795; Testing set: AUC 0.720) and radiomics model (Training set: AUC 0.803; Testing set: AUC 0.759), the nomogram demonstrated superior discriminative ability on both the training (AUC 0.884) and testing (AUC 0.820) datasets. Calibration curves indicated high consistency between the nomogram model's predicted probability of breast cancer TIL levels and the actual occurrence probability. DCA revealed that the radiomics model and the nomogram model achieved higher clinical net benefits compared to the grayscale ultrasound model.

Conclusion

The nomogram based on preoperative ultrasound radiomics features exhibits robust predictive capacity for the non-invasive evaluation of breast cancer TIL levels, potentially providing a significant basis for individualized treatment decisions in breast cancer.

Evaluation of Breast Cancer Tumor-Infiltrating Lymphocytes on Ultrasound Images Based on a Novel Multi-Cascade Residual U-Shaped Network

OBJECTIVE

Breast cancer has become the leading cancer of the 21st century. Tumor-infiltrating lymphocytes (TILs) have emerged as effective biomarkers for predicting treatment response and prognosis in breast cancer. The work described here was aimed at designing a novel deep learning network to assess the levels of TILs in breast ultrasound images.

METHODS

We propose the Multi-Cascade Residual U-Shaped Network (MCRUNet), which incorporates a gray feature enhancement (GFE) module for image reconstruction and normalization to achieve data synergy. Additionally, multiple residual U-shaped (RSU) modules are cascaded as the backbone network to maximize the fusion of global and local features, with a focus on the tumor's location and surrounding regions. The development of MCRUNet is based on data from two hospitals and uses a publicly available ultrasound data set for transfer learning.

RESULTS

MCRUNet exhibits excellent performance in assessing TILs levels, achieving an area under the receiver operating characteristic curve of 0.8931, an accuracy of 85.71%, a sensitivity of 83.33%, a specificity of 88.64% and an F1 score of 86.54% in the test group. It outperforms six state-of-the-art networks in terms of performance.

CONCLUSION

The MCRUNet network based on breast ultrasound images of breast cancer patients holds promise for non-invasively predicting TILs levels and aiding personalized treatment decisions.

Tumor infiltrating lymphocytes and radiological picture of the tumor

Tumor microenvironment (TME) is a complex entity that includes besides the tumor cells also a whole range of immune cells. Among various populations of immune cells infiltrating the tumor, tumor infiltrating lymphocytes (TILs) are a population of lymphocytes characterized by high reactivity against the tumor component. As, TILs play a key role in mediating responses to several types of therapy and significantly improve patient outcomes in some cancer types including for instance breast cancer and lung cancer, their assessment has become a good predictive tool in the evaluation of potential treatment efficacy. Currently, the evaluation of the density of TILs infiltration is performed by histopathological. However, recent studies have shed light on potential utility of several imaging methods, including ultrasonography, magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), and radiomics, in the assessment of TILs levels. The greatest attention concerning the utility of radiology methods is directed to breast and lung cancers, nevertheless imaging methods of TILs are constantly being developed also for other malignancies. Here, we focus on reviewing the radiological methods used to assess the level of TILs in different cancer types and on the extraction of the most favorable radiological features assessed by each method.

Evaluating Tumor-Infiltrating Lymphocytes in Breast Cancer: The Role of Conventional Ultrasound and Contrast-Enhanced Ultrasound

OBJECTIVES

Tumor-infiltrating lymphocytes (TILs) have emerged as an efficient biomarker predicting treatment response and prognosis of breast cancer (BC). This study aimed to evaluate the association between conventional ultrasound and contrast-enhanced ultrasound (CEUS) imaging features with TIL levels in invasive BC patients.

METHODS

We retrospectively included 267 women with invasive BC who had undergone conventional ultrasound and CEUS. Patients were divided into low (≤10%) and high (>10%) TIL groups. Conventional ultrasound and CEUS features were analyzed by two sonographers. The associations between the TIL levels and imaging features were evaluated.

RESULTS

Of the 267 patients, 122 with high TILs and 145 with low TIL levels. High TIL tumors were more likely to have a circumscribed margin, oval or round shape, and enhanced posterior echoes on ultrasonography (p < 0.05). In contrast, low TIL tumors were more likely to have an irregular shape, un-circumscribed, indistinct and spiculated margin (p < 0.05). In CEUS, high TIL tumors showed a more regular shape, clearer margin, more homogeneous enhancement and higher peak intensity (PI) value (p < 0.05). Logistic analysis indicated that shape, posterior features, PI, and enhanced homogeneity were independent predictors for high TIL tumors. The model combined the four independent predictors have a moderate performance in predicting high TIL tumors with AUC 0.79, sensitivity 0.72, and specificity 0.78.

CONCLUSIONS

Conventional ultrasound and CEUS features were associated with TIL levels in invasive BC. Consequently, the results suggested that preoperative conventional ultrasound and CEUS may be a useful noninvasive imaging biomarker for individualized treatment decisions.

Deep Learning with Transformer or Convolutional Neural Network in the Assessment of Tumor-Infiltrating Lymphocytes (TILs) in Breast Cancer Based on US Images: A Dual-Center Retrospective Study

This study aimed to explore the feasibility of using a deep-learning (DL) approach to predict TIL levels in breast cancer (BC) from ultrasound (US) images. A total of 494 breast cancer patients with pathologically confirmed invasive BC from two hospitals were retrospectively enrolled. Of these, 396 patients from hospital 1 were divided into the training cohort (n = 298) and internal validation (IV) cohort (n = 98). Patients from hospital 2 (n = 98) were in the external validation (EV) cohort. TIL levels were confirmed by pathological results. Five different DL models were trained for predicting TIL levels in BC using US images from the training cohort and validated on the IV and EV cohorts. The overall best-performing DL model, the attention-based DenseNet121, achieved an AUC of 0.873, an accuracy of 79.5%, a sensitivity of 90.7%, a specificity of 65.9%, and an F1 score of 0.830 in the EV cohort. In addition, the stratified analysis showed that the DL models had good discrimination performance of TIL levels in each of the molecular subgroups. The DL models based on US images of BC patients hold promise for non-invasively predicting TIL levels and helping with individualized treatment decision-making.

Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy

Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.

The tumor-infiltrating lymphocyte ultrasonography score can provide a diagnostic prediction of lymphocyte-predominant breast cancer preoperatively

PURPOSE

Tumor-infiltrating lymphocytes (TILs) are known to predict the therapeutic effect in breast cancer. Although a preoperative tissue biopsy can be used to evaluate TILs, TILs that are heterogeneously distributed might require examination of all preoperative tissue biopsy samples. We have recently reported that the TIL ultrasonography (US) score, as determined by characteristic US findings, provides excellent predictive performance for lymphocyte predominant breast cancer (LPBC). We herein aimed to determine whether the preoperative TIL-US score can more accurately predict LPBC than preoperative tissue biopsy.

METHODS

We assessed 161 patients with invasive breast cancer that were treated with curative surgery between January 2014 and December 2017. Stromal lymphocytes were examined on preoperative tissue biopsy tissues and surgical pathological specimens. Breast cancer samples with ≥ 50% stromal TILs were defined as pre-LPBC (preoperative tissue biopsy) and LPBC (surgical pathological specimens). Useful factors for predicting LPBC were searched among clinicopathological factors.

RESULTS

The TIL-US score cutoff value for predicting LPBC was 4 points based on the receiver operating characteristic curves (area under the curve: 0.88). Several significant predictors for LPBC were revealed by the undertaken multivariate logistic regression analysis (odds ratios: TIL-US score, 26.8; pre-LPBC, 18.6; HER2, 9.2; all, p < 0.05). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.74, 0.89, 0.85, 0.67, and 0.92 for the TIL-US score, respectively, and 0.51, 0.98, 0.87, 0.91, and 0.86 for the pre-LPBC, respectively.

CONCLUSION

TIL-US scores can predict LPBC preoperatively and are characterized by a significantly high sensitivity and negative predictive value.

Tumor-infiltrating lymphocytes in breast FNA biopsy cytology: a predictor of tumor-infiltrating lymphocytes in histologic evaluation

BACKGROUND

Tumor-infiltrating lymphocytes (TILs) are associated with various clinicopathological features. Using cytologic specimens for assessing TILs remains to be established. This retrospective study aimed to establish a practical method to assess TILs in cytologic samples.

METHODS

The authors found 1101 breast fine-needle aspiration biopsy (FNAB) cytology samples in their hospital, and 214 of them met the inclusion criteria. The TILs score was evaluated using histologic slides, and breast cancers were divided into 2 groups: low- (<60%) and high-TILs (≥60%). Training and validation tests composed of 50 breast cancer samples each were constructed. A cytologic TILs (cTILs) score was introduced to evaluate lymphocytes in FNAB cytology and it was compared with histologically evaluated TILs. The cTILs score was calculated by subtracting the number of neutrophils from the number of lymphocytes surrounding the tumor cells.

RESULTS

In the training test, a 2-tier system with low- and high-TILs groups showed a large area under the curve (AUC) (0.943; 95% confidence interval [CI], 0.84-0.99). A cTILs score cutoff value of >8 had 87.5% sensitivity and 90.5% specificity. In the validation test, the AUC was 0.79 (95% CI, 0.6-0.93) whereas sensitivity and specificity were 57% and 89.5%, respectively. When small tumors <0.5 cm were excluded, the AUC improved to 0.93 (95% CI, 0.83-1.0), and sensitivity and specificity were 80% and 88.5%, respectively.

CONCLUSIONS

The cTILs scoring system had acceptable reproducibility and concordance with TILs on histologic samples for tumors ≥0.5 cm. Cytologic evaluation can potentially substitute for histologic evaluation of TILs.

BI-RADS Ultrasound Lexicon Descriptors and Stromal Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer

PURPOSE

Increased levels of stromal tumor-infiltrating lymphocytes (sTILs) have recently been considered a favorable independent prognostic and predictive biomarker in triple-negative breast cancer (TNBC). The purpose of this study was to determine the relationship between BI-RADS (Breast Imaging Reporting and Data System) ultrasound lexicon descriptors and sTILs in TNBC.

MATERIALS AND METHODS

Patients with stage I-III TNBC were evaluated within a single-institution neoadjuvant clinical trial. Two fellowship-trained breast radiologists used the BI-RADS ultrasound lexicon to assess pretreatment tumor shape, margin, echo pattern, orientation, posterior features, and vascularity. sTILs were defined as low <20 or high ≥20 on the pretreatment biopsy. Fisher's exact tests were used to assess the association between lexicon descriptors and sTIL levels.

RESULTS

The 284 patients (mean age 52 years, range 24-79 years) were comprised of 68% (193/284) with low-sTIL tumors and 32% (91/284) with high-sTIL tumors. TNBC tumors with high sTILs were more likely to have the following features: (1) oval/round shape than irregular shape (p = 0.003), (2) circumscribed or microlobulated margins than spiculated, indistinct, or angular margins (p = 0.0005); (3) complex cystic and solid pattern than heterogeneous pattern (p = 0.006); and (4) posterior enhancement than shadowing (p = 0.002). There was no significant association between sTILs and descriptors for orientation and vascularity (p = 0.06 and p = 0.49, respectively).

CONCLUSION

BI-RADS ultrasound descriptors of the pretreatment appearance of a TNBC tumor can be useful in discriminating between tumors with low and high sTIL levels. Therefore, there is a potential use of ultrasound tumor characteristics to complement sTILs when used as stratification factors in treatment algorithms for TNBC.

Ultrasonography Combined With Contrast-enhanced Ultrasonography Can Predict Lymphocyte-predominant Breast Cancer

BACKGROUND

We investigated whether contrast-enhanced ultrasonography (CEUS) scores can predict lymphocyte-predominant breast cancer (LPBC).

PATIENTS AND METHODS

We evaluated 75 patients who underwent US and CEUS. LPBC was defined as tissues with ≥50% stromal tumour-infiltrating lymphocytes (TILs) preoperatively. Characteristic US images predicting LPBC were evaluated using TIL-US scores via three ultrasonic tissue characteristics: Shape, internal echo level, and posterior echoes. TIL-CEUS was evaluated based on TIL-US plus CEUS.

RESULTS

TIL-US and TIL-CEUS cut-offs for predicting LPBC were 4 and 6 (area under the curve=0.93 and 0.96, respectively) points based on receiver operating characteristics curves. Sensitivity, specificity, and accuracy values (95% confidence intervaI) were 0.94 (0.77-0.99), 0.75 (0.70-0.77), and 0.80 (0.72-0.82); and 0.94 (0.78-0.99), 0.86 (0.81-0.87), and 0.88 (0.80-0.90) for TIL-US and TIL-CEUS, respectively. TIL-CEUS score was a significant single predictor for LPBC in multivariate logistic regression (p=0.001).

CONCLUSION

TIL-CEUS can be used for preoperative LPBC prediction and detection.

Usefulness of imaging findings in predicting tumor-infiltrating lymphocytes in patients with breast cancer

OBJECTIVES

Tumor-infiltrating lymphocytes (TILs) have been determined as a new prognostic indicator of immunotherapy response in breast cancer (BC). The aim of this study is to investigate the effectiveness of imaging features in predicting the TIL levels in invasive BC patients.

METHODS

A total of 158 patients with invasive BC were included in our study. All lesions were evaluated based on the BIRADS lexicon. US was performed for all the patients and 89 of them underwent MRI. The histologic stromal TIL (sTIL) levels were assessed and associations between the sTIL levels and imaging features were evaluated.

RESULTS

Tumors with high sTIL levels had more circumscribed margins, round shape, heterogeneous echogenicity, and larger size on ultrasonography (p < 0.005). There was a statistically significant positive correlation between the sTIL levels and ADC value (p < 0.001). Tumors with high sTIL levels had significantly more homogeneous enhancement than the tumors with low sTIL levels (p = 0.001). Logistic regression analysis showed that the ADC was the most statistically significant parameter in predicting the sTIL levels (the odds ratio was 90.952; p = 0.002). The optimal cutoff value for ADC in predicting low and high sTIL levels was found to be 0.87 × 10^-3 mm² s^-1 (AUC = 0.726, 73% specificity, and 60% sensitivity).

CONCLUSIONS

Imaging findings, especially the ADC, may play an important role as an adjunct tool in cases of uncertain situations and may improve the accuracy of biopsy results. The prediction of sTIL levels using imaging findings may give an opportunity to predict prognosis.

KEY POINTS

• Preoperative assessment of TILs is an important biomarker of prognosis and treatment efficacy. • ADC value can be a useful tool in distinguishing high and low sTIL levels as a non-invasive method. • The prediction of sTIL levels using imaging findings may give an opportunity to predict prognosis and an optimal treatment for the BC patients.