diff --git a/monai/transforms/spatial/dictionary.py b/monai/transforms/spatial/dictionary.py
index 51ad0435fc..543d875c4e 100644
--- a/monai/transforms/spatial/dictionary.py
+++ b/monai/transforms/spatial/dictionary.py
@@ -2305,12 +2305,13 @@ def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> dict[Hashable, torc
         d = dict(data)
         self.randomize(None)
         if not self._do_transform:
-            out: dict[Hashable, torch.Tensor] = convert_to_tensor(d, track_meta=get_track_meta())
-            return out
+            for key in self.key_iterator(d):
+                d[key] = convert_to_tensor(d[key], track_meta=get_track_meta())
+            return d
 
         first_key: Hashable = self.first_key(d)
         if first_key == ():
-            out = convert_to_tensor(d, track_meta=get_track_meta())
+            out: dict[Hashable, torch.Tensor] = convert_to_tensor(d, track_meta=get_track_meta())
             return out
         if isinstance(d[first_key], MetaTensor) and d[first_key].pending_operations:  # type: ignore
             warnings.warn(f"data['{first_key}'] has pending operations, transform may return incorrect results.")
diff --git a/tests/transforms/test_rand_grid_distortiond.py b/tests/transforms/test_rand_grid_distortiond.py
index 8f8de144f6..d1de93ea95 100644
--- a/tests/transforms/test_rand_grid_distortiond.py
+++ b/tests/transforms/test_rand_grid_distortiond.py
@@ -85,6 +85,15 @@ def test_rand_grid_distortiond(self, input_param, seed, input_data, expected_val
         assert_allclose(result["img"], expected_val_img, type_test=False, rtol=1e-4, atol=1e-4)
         assert_allclose(result["mask"], expected_val_mask, type_test=False, rtol=1e-4, atol=1e-4)
 
+    def test_no_transform_preserves_non_image_keys(self):
+        """Non-image dict entries must not be coerced to tensors when the transform is skipped."""
+        img = np.indices([6, 6]).astype(np.float32)
+        data = {"img": img, "label": 42, "filename": "scan.nii"}
+        g = RandGridDistortiond(keys=["img"], prob=0.0)
+        result = g(data)
+        self.assertIsInstance(result["label"], int)
+        self.assertIsInstance(result["filename"], str)
+
 
 if __name__ == "__main__":
     unittest.main()