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Multimodal nomogram predicts axillary pathological complete response in node-positive breast cancer patientsCan scans stop unnecessary breast surgery?

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Key Takeaway
Consider using this multimodal nomogram to identify patients with axillary pCR who may avoid unnecessary ALND.

This retrospective study analyzed data from 600 patients with node-positive breast cancer (clinical stage cT1–4 and cN1-3) who received neoadjuvant therapy between January 2011 and January 2024. The primary objective was to assess a multimodal nomogram that integrated ultrasonography, magnetic resonance imaging, and clinicopathological features to predict axillary pathological complete response (pCR). No comparator group was reported for this predictive modeling exercise.

The analysis identified independent predictors of axillary lymph node pCR after neoadjuvant therapy. These included ypT2 (p < 0.001), ypT3 (p = 0.007), HER2 status (p < 0.001), response PR (p = 0.007), efficacy evaluation showing SD/PD (p = 0.010), and the presence of the Hilum of the lymph gland on ultrasonography after neoadjuvant therapy (p < 0.001). The nomogram achieved an area under the curve (AUC) of 0.934 (95% CI: 0.913-0.960) in the training set and 0.908 (95% CI: 0.867-0.950) in the validation set. Sensitivity was 82.0% and specificity was 89.1% in the training set.

Safety data, including adverse events, discontinuations, or tolerability, were not reported for this study. Key limitations include the retrospective nature of the data collection, the absence of a comparator group, and the lack of reported follow-up duration. Additionally, the study phase and publication type were not reported. The model's performance relies on specific imaging and pathological features that must be available in the clinical setting.

In practice, this model may help accurately identify patients with axillary lymph node pCR after neoadjuvant therapy. Such identification could potentially prevent unnecessary axillary lymph node dissection in selected patients. However, clinicians should interpret these results cautiously given the observational design and the absence of external validation data beyond the internal validation set.

Doctors now have a better way to see if cancer is truly gone before cutting.

Breast cancer is common, but the treatment for the armpit area is often too aggressive. Many women with node-positive disease get a full removal of lymph nodes just to be safe. This surgery causes lifelong swelling and pain.

Current tests often guess wrong. They might say cancer is still there when it isn't. Or they miss it when it is.

The surprising shift

For years, surgeons waited for surgery to know the truth. They removed nodes to check for hidden cancer cells. But this study changes that timeline.

Researchers built a new tool. It combines ultrasound, MRI, and lab data. This mix acts like a super-powered detective. It looks at the tumor and the lymph nodes before the knife ever touches the patient.

What scientists didn't expect

The team looked at 600 patients. They tracked them from 2011 to 2024. They used two types of scans and blood test markers.

They found a specific sign in the lymph nodes. If the "hilum" (the center part) stayed visible on an ultrasound after treatment, it was a huge green flag. It meant the cancer was likely gone.

A simple analogy

Think of the lymph node like a busy intersection. Cancer cells are cars clogging the road.

When treatment works, the traffic clears up. The center of the intersection becomes visible again. If the center is still dark and blocked, the road is still jammed with cancer.

This visual clue is easier to spot than just guessing based on size alone.

The researchers studied women with early-stage breast cancer. They had enlarged lymph nodes in their armpits.

Everyone received chemotherapy or radiation first. Then they got scans again. Two expert radiologists read every single image. They agreed on the results to avoid mistakes.

The new tool is very accurate. It correctly identified who had a complete response. That means no cancer cells were left behind.

The tool worked 93% of the time in the main group. It worked 91% of the time in a separate check group.

This accuracy is high. It means doctors can trust the scan more than their gut feeling.

This doesn't mean this treatment is available yet.

The tool is a prediction model. It helps plan surgery, but it is not a new drug. It uses existing technology better.

Doctors say this fits perfectly into current care. It reduces the need for full node removal. Fewer surgeries mean less swelling and better quality of life.

It also helps avoid missing cancer. If the scan says cancer is gone, the doctor can be more confident.

If you have breast cancer, talk to your doctor about imaging. Ask if a second look at the lymph nodes is possible after chemo.

This model suggests that some women might skip a major surgery. But only a doctor can decide this for you. Do not stop treatment based on a scan alone.

This study looked at past patients. It was done at one center. The tool needs more testing in different hospitals.

Also, the scans must be read by experts. A regular scan might not show the tiny details needed.

More trials are needed to prove this works everywhere. Regulatory agencies will review the data before it changes standard guidelines.

Until then, this research gives hope. It shows we can be smarter about surgery. We can spare women from unnecessary pain while keeping them safe.

Study Details

Study typeCohort
EvidenceLevel 3
PublishedApr 2026
View Original Abstract ↓
ObjectivesThis study aims to create a model that combines ultrasonography (US), magnetic resonance imaging (MRI) examination, and clinicopathological features to predict the axillary pathological complete response (pCR) of patients with breast cancer (BC) who receive neoadjuvant therapy (NAT).MethodsThis retrospective study included 600 patients with node-positive breast cancer who were eligible for enrollment (clinical stage cT1–4 and cN1-3) and received neoadjuvant therapy from January 2011 to January 2024. Before biopsy and neoadjuvant therapy, these patients underwent ultrasound (US) and MRI imaging of breast lesions and axillary lymph nodes (ALNs), and clinicopathological features were recorded before and after NAT. All imaging evaluations were independently performed by two experienced breast radiologists (with >10 years of experience), and discrepancies were resolved by consensus. Independent risk factors for predicting ALN status after NAT were identified by univariate and multivariate analyses. These independent risk factors were used for nomogram construction.ResultsUnivariate logistic regression analysis revealed that the maximum diameter of the breast lesions on MRI after NAT (p < 0.001), MRI ADC-value after NAT (p < 0.001), maximum and minimum diameter of the ALN on US after NAT (p < 0.001), the Ki67 level (p < 0.001), tumor grade 3 (p = 0.017), primary ALN stage cN 2 (p = 0.022), efficacy evaluation of the neoadjuvant therapy, pT stage, MP classification, HR, HER2, and the presence of the Hilum of the lymph gland were significantly associated with ALN pCR after NAT (p < 0.05). In the multivariate logistic regression analysis, ypT2 (p < 0.001), ypT3 (p = 0.007), HER2 (p < 0.001), response PR (p = 0.007), efficacy evaluation (SD/PD) (p = 0.010), and the presence of the Hilum of the lymph gland on US after NAT(p < 0.001) were considered independent predictors of ALN pCR after NAT. The area under the curve (AUC) of the nomogram was 0.934(95% CI: 0.913-0.960) in the training set and 0.908 (95% CI: 0.867-0.950) in the validation set, with a sensitivity of 82.0% and a specificity of 89.1% in the training set.ConclusionOur noninvasive model based on US, MRI, and clinicopathological features can help accurately identify patients with ALN pCR after NAT and prevent unnecessary axillary lymph node dissection (ALND).
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